Diagnostic and prognostic methods for cardiovascular diseases and events

ABSTRACT

Compositions and methods are provided for diagnosis and/or prognosis of cardiovascular diseases or events in a subject. In some embodiments, the method includes measuring and comparing the level of particular proteins to other proteins. In other embodiments, the method includes comparison with clinical variable information.

CROSS REFERENCED RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 16/073,754, filed on Jul. 27, 2018, which is a 371 of internationalapplication number PCT/US2017/016081, filed Feb. 1, 2017, which claimspriority to, and benefit of, U.S. Provisional Application No. 62/289,513filed Feb. 1, 2016 and U.S. Provisional application No. 62/378,535 filedAug. 23, 2016, the contents of which are incorporated herein byreference in their entireties.

FIELD OF THE INVENTION

The present disclosure relates biomarker panels, assays, and kits andmethods for determining the diagnosis and/or prognosis of acardiovascular disease or outcome in a patient.

BACKGROUND OF THE INVENTION

Atherosclerotic cardiovascular disease (ASCVD) and its associatedcardiovascular events (CVE) including, for example, obstructive coronaryartery disease (CAD), myocardial infarction (MI), stroke, andcardiovascular death (CVD) are predominantly caused by an underlyingvascular endothelial process leading to deposition of lipid material andother proteins resulting in atherosclerotic plaque formation in multiplevascular beds in the human body. While this process is associated withidentifiable and modifiable risk factors, the disease process and itsrelated events noted above remain the leading cause of death and severedisability worldwide (Yusuf et al., Effect of potentially modifiablerisk factors associated with myocardial infarction in 52 countries (theINTERHEART study): case-control study, Lancet, 364:937-52 (2004)).

In the Western world, cardiovascular disease, typically associated withunderlying atherosclerosis, is the leading cause of death(Martin-Ventura et al., 2009, Rev. Esp. Cardiol 62(6):677-688, citingMurray and Lopez, 1997, Lancet 349:1269-1276). Risk factors for cardiacdisease are well known, and include hypertension, diabetes, smoking,elevated cholesterol, obesity, and family history. However, despite theprevalence of obstructive coronary artery disease (CAD) and theappreciation of its risk factors, the link between the onset of symptomsand a cardiac event requiring intervention remains elusive. Symptoms canbe non-specific, such as a feeling of heaviness in the chest, and canreflect CAD but could also be explained by gastric distress; pain in theleft arm could be of cardiac origin or could be caused by arthritis.Even when pain is highly likely to be cardiac in origin, there can bequestions regarding the type and intensity of treatment required; insome scenarios, medication may be sufficient, but in others, aninterventional strategy is necessary to avoid CVE.

A number of technologies have been developed to identify patients athigh risk for an adverse cardiac event. These include exercise andpharmacologic stress testing using evaluations of the ECG response, thecardiac wall motion response using ultrasound, and changes in myocardialperfusion using nuclear imaging techniques. Coronary angiography, themost invasive approach, has been considered the “gold standard”diagnostic tool to evaluate coronary artery anatomy, cardiac structure,and function, but it is invasive, costly, has defined complications, andis subject to operator-dependent variability (Sharma et al., 2010, Vasc.Health Risk Manag. 6:307-316). Other, newer and less-invasive optionsare being explored, including coronary computed tomographic angiography(Sharma et al., supra; Cury et al., 2008. J. Nucl. Cardiol.15(4):564-575), biomarkers (e.g., Martin-Ventura et al., 2009, Rev. Esp.Cardiol 62(6′):677-688), adenosine stress magnetic resonance perfusionimaging (Ingkanisorn et al., 2006, J. Am. Coll. Cardiol. 47(7):1427-1432), the use of clinical predictors (Tadros et al., 2003. SouthMed. J. 96(11):1113-1120; Schillinger et al., 2004, Wien Klin.Wochenschr. 116(3):83-89), and indicators of platelet activity (Marcucciet al., 2009, Circulation 119:237-242 (originally published online Dec.31, 2008); Selvaraj et al., 2004, J. Throm. Thrombolysis 18(2):109-115).

A need therefore exists for a simple and reliable method to improve thediagnosis of cardiovascular pathologies and the prediction of CVE.

SUMMARY OF THE INVENTION

The present disclosure provides methods for determining the diagnosisand/or prognosis of a cardiovascular disease or outcome in a subject,comprising the steps: (i) determining the level of at least one, atleast two, at least three, at least four or greater than four biomarkersin a biological sample obtained from the subject, wherein the biomarkersare selected from the group consisting of those set forth in Tables 1A,1B, 2A, and 2B; (ii) optionally, determining the status of at least oneclinical variable for the subject, wherein the clinical variable isselected from the group consisting of those set forth in Tables 3A, 3B,4A and 4B; (iii) calculating a diagnostic and/or prognostic score forthe subject based on the determined level of at least one biomarker and,optionally, the status of the clinical variable(s) determined in step(ii); (iv) classifying the diagnostic or prognostic score as a positiveor negative result; and (v) determining a therapeutic or diagnosticintervention regimen based on the positive or negative result.

The diagnosis or prognosis provided by the methods of the presentdisclosure are particularly important in defining and determining atherapeutic path forward for a patient receiving a positive diagnosis ofcoronary artery disease and/or a positive prognosis of cardiovasculardeath, myocardial infarct (MI), stroke, all cause death, or a compositethereof. In this way, the determined diagnosis and/or prognosis of acardiovascular disease or outcome in the subject facilitates adetermination by a medical practitioner of a need for a therapeutic ordiagnostic intervention in the subject.

In certain more specific embodiments, the biomarkers used in the methodsare selected from the biomarkers listed in Tables 1A, 1B, 2A, and 2B,particularly those that have a p-value of less than 0.1, less than 0.05,less than 0.01 or less than 0.001.

In a particular embodiment, the method comprises determining the levelsof at least one, at least two, at three, at least four, or greater thanfour biomarkers selected from the group consisting of adiponectin,apolipoprotein A-II, apolipoprotein C-I, decorin, interleukin-8, kidneyinjury molecule-1, matrix metalloproteinase 9 (MMP-9), midkine,myoglobin, N terminal prohormone of brain natriuretic protein(NT-proBNP), osteopontin, pulmonary surfactant associated protein D,stem cell factor, tissue inhibitor of metalloproteinases-1 (TIMP-1),troponin, and vascular cell adhesion molecule (VCAM).

In still other embodiments, in addition to determining biomarker levelsin the biological sample, the method further comprises determining thestatus of at least one clinical variable, such as those listed in Tables3A, 3B, 4A and 4B, particularly those having a p-value of less than 0.1,less than 0.05, less than 0.01, or less than 0.001.

In a more particular embodiment, the method comprises determining thestatus of at least one clinical variable selected from the groupconsisting of age, history of coronary artery bypass graft surgery(CABG), history of diabetes type 2, history of hemodialysis, history ofmyocardial infarct (MI), history of percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), and sex.

In still other embodiments of the present disclosure, step (i) of themethod comprises determining the level of at least one, at least two, atleast three, at least four, or greater than four biomarkers selected thegroups consisting of adiponectin, apolipoprotein A-II, apolipoproteinC-I, decorin, interleukin-8, kidney injury molecule-1, matrixmetalloproteinase 9 (MMP-9), midkine, myoglobin, N terminal prohormoneof brain natriuretic protein (NT-proBNP), osteopontin, pulmonarysurfactant associated protein D, stem cell factor, tissue inhibitor ofmetalloproteinases-1 (TIMP-1), troponin, and vascular cell adhesionmolecule (VCAM), and step (ii) comprises determining the status of atleast one clinical variables selected from age, history of coronaryartery bypass graft surgery (CABG), history of diabetes type 2, historyof hemodialysis, history of myocardial infarct (MI), history ofpercutaneous coronary intervention (e.g. balloon angioplasty with orwithout stent placement), and sex.

The methods advantageously provide a diagnosis of obstructive coronaryartery disease in the subject. In certain more specific embodiments, thediagnosis of obstructive coronary artery disease in the subjectcomprises a diagnosis of 70% or greater obstruction in a majorepicardial vessel.

In still additional embodiments of the disclosure, step (i) of themethod comprises determining the level of at least one, at least two, atleast three, at least four, or greater than four biomarkers selectedfrom the group consisting of adiponectin, apolipoprotein C-I, decorin,interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9,midkine, myoglobin, pulmonary surfactant associated protein D, stem cellfactor, and troponin.

In further particular embodiments, the methods provide a prognosis ofthe likelihood for a cardiac outcome, such as an outcome selected fromcardiovascular death, myocardial infarct (MI), stroke, all cause death,or a composite thereof.

According to another aspect of the present provides methods fordiagnosing the presence of obstructive coronary artery disease in asubject, comprising the steps: (i) determining the level of at leastone, at least two, at least three, at least four, or greater than fourbiomarkers in a biological sample obtained from the subject, wherein thebiomarkers are selected from the group consisting of the biomarkerslisted in Tables 1A and 1B; (ii) optionally determining the status of atleast one clinical variable for the subject, wherein the clinicalvariable is selected from the group consisting of the clinical variableslisted in Tables 3A and 3B; (iii) calculating a diagnostic score for thesubject based on the determined level of the at least one biomarker andoptionally the status of the at least one clinical variable; (iv)classifying the score as a positive or negative diagnosis of obstructivecoronary artery disease; and (iv) determining a therapeutic ordiagnostic intervention regimen based on the positive or negativediagnosis.

In certain more specific embodiments, the biomarkers are selected fromthose listed in Tables 1A and 1B having p-values of less than 0.1, lessthan 0.05, less than 0.01 or less than 0.001.

In other more specific embodiments, step (i) of the method comprisesdetermining the levels at least one, at least two, at least three, atleast four, or greater than four biomarkers selected from the groupconsisting of adiponectin, apolipoprotein C-I, decorin, interleukin-8,kidney injury molecule-1, matrix metalloproteinase 9, midkine,myoglobin, pulmonary surfactant associated protein D, stem cell factor,and troponin.

In other embodiments, the clinical variable(s) assessed according to themethod is selected from those listed in Tables 3A and 3B having p-valuesof less than 0.1, less than 0.05, less than 0.01 or less than 0.001.

In still other embodiments, step (ii) of the method comprisesdetermining the status of at least one clinical variables selected fromthe group consisting of age, history of coronary artery bypass graftsurgery (CABG), history of diabetes type 2, history of hemodialysis,history of myocardial infarct (MI), history of percutaneous coronaryintervention (e.g., balloon angioplasty with or without stentplacement), and sex.

In further embodiments, step (i) of the method comprises determining thelevels at least one, at least two, at least three, at least four, orgreater than four biomarkers selected the groups consisting ofadiponectin, apolipoprotein C-I, decorin, interleukin-8, kidney injurymolecule-1, matrix metalloproteinase 9, midkine, myoglobin, pulmonarysurfactant associated protein D, stem cell factor, and troponin and step(ii) comprises determining the status of at least one clinical variablesselected from age, history of coronary artery bypass graft surgery(CABG), history of diabetes type 2, history of hemodialysis, history ofmyocardial infarct (MI), history of percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), and sex.

In other embodiments, a positive diagnosis for obstructive coronaryartery disease in the subject facilitates a determination by a medicalpractitioner of the need for an intervention or further testing. Anintervention or further testing may include but are not limited to oneor more of a diagnostic cardiac catheterization (also referred to as“cath”), percutaneous coronary intervention (balloon angioplasty with orwithout stent placement), coronary artery bypass graft (CABG), andadministration of pharmacologic agents, such as one selected from one ormore of nitrates, beta blockers, ACE inhibitor and lipid-loweringagents.

In other embodiments, a negative diagnosis for obstructive coronaryartery disease in the subject facilitates a determination by a medicalpractitioner of the need for an intervention or further testing. Anintervention or further testing may include but are not limited to oneor more of ongoing monitoring and management of coronary risk factorsincluding hypertension, diabetes, and smoking, and lifestylemodifications selected from diet modification, exercise, and smokingcessation.

Another aspect of the present disclosure provides methods for theprognosis of a cardiac outcome in a subject, comprising the steps: (i)determining the level of at least one biomarker in a biological sampleobtained from the subject, wherein the biomarkers are selected from thegroup consisting of the biomarkers listed in Tables 2A and 2B; (ii)optionally determining the status of at least one clinical variable forthe subject, wherein the clinical variable is selected from the groupconsisting of the clinical variables listed in Tables 4A and 4B; (iii)calculating a prognostic score for the subject based on the determinedlevels of the at least one biomarker and, optionally, the status of theclinical variable(s) determined in step (ii); (iv) classifying theprognostic score as a positive or negative prognosis; and (v)determining a therapeutic or diagnostic intervention regimen based onthe positive or negative prognosis.

In more specific embodiments, the biomarkers evaluated in the method areselected from those listed in Tables 2A and 2B having p-values of lessthan 0.1, less than 0.05, less than 0.01 or less than 0.001.

In other embodiments, the clinical variable(s) assessed according to themethod is selected from those listed in Tables 4A and 4B having p-valuesof less than 0.1, less than 0.05, less than 0.01 or less than 0.001.

In other specific embodiments, step (i) of the method comprisesdetermining the levels of at least one, at least two, at least three, atleast four, or greater than four biomarkers selected from the groupconsisting of apolipoprotein A-II, kidney injury molecule-1, midkine, Nterminal prohormone of brain natriuretic protein (NT-proBNP),osteopontin, tissue inhibitor of metalloproteinases-1 (TIMP-1), andvascular cell adhesion molecule (VCAM).

In still other embodiments, the prognosis of a cardiac outcome is aprognosis of cardiovascular death, myocardial infarct (MI), stroke, allcause death, or a composite thereof.

In additional embodiments, a positive prognosis of a cardiac outcomefacilitates a determination by a medical practitioner of the need for anintervention or further testing. An intervention or further testing mayinclude but are not limited to one or more of stress testing with ECGresponse or myocardial perfusion imaging, coronary computed tomographyangiogram, diagnostic cardiac catheterization, percutaneous coronaryintervention (e.g., balloon angioplasty with or without stentplacement), coronary artery bypass graft (CABG), enrollment in aclinical trial, and administration or monitoring of effects of agentsselected from, but not limited to, nitrates, beta blockers, ACEinhibitors, antiplatelet agents and lipid-lowering agents.

In other embodiments, a negative prognosis of a cardiac outcomefacilitates a determination by a medical practitioner of the need for anintervention or further testing. An intervention or further testing mayinclude but are not limited to one or more of ongoing monitoring andmanagement of coronary risk factors including hypertension, diabetes,hyperlipidemia and smoking; and lifestyle modifications selected fromdiet modification, exercise and smoking cessation.

In other aspects, the present disclosure provides a diagnostic orprognostic kit comprising a panel of biomarkers and optionally clinicalvariables as described.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a receiver operating characteristic curve for the PrevencioCAD panel FM139/685 (as described in Example 1), in the internalvalidation set (N=278) to diagnose the presence of severe CAD (≥70%stenosis in any vessel). The panel had a robust area under the curve(AUC) of 0.87.

FIG. 2 shows a distribution of the CAD panel FM139/685 (as described inExample 1), in the internal validation set (N=278) to diagnose thepresence of severe CAD (>70% stenosis in any vessel). A bimodaldistribution is noted, with preponderance of those with significant CADdistributed at higher scores. Positive=subjects with at least onecoronary stenosis ≥70%, negative=subjects with no coronary stenoses≥70%.

FIG. 3 shows results from dividing the CAD panel FM139/685 (as describedin Example 1), in the internal validation set (N=278) into 5 categoriesof predicted likelihood of CAD (≥70% stenosis in any vessel). In doingso, 42% of subjects could be “ruled in” or “ruled out” for severe CADwith a PPV of 93% and a NPV of 91%, respectively.

FIG. 4 shows Kaplan-Meier survival curves depicting time to incidentacute MI as a function of CAD Score in panel FM139/685 (as described inExample 1). Though developed as a diagnostic tool for CAD, the scorealso presaged incident acute MI during follow-up.

FIG. 5 shows a receiver operating characteristic curve for the PrevencioCAD panel FM144/696 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.87.

FIG. 6 shows a receiver operating characteristic curve for the PrevencioCAD panel FM145/701 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hadan area under the curve (AUC) of 0.72.

FIG. 7 shows a receiver operating characteristic curve for the PrevencioCAD panel FM146/690 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hadan area under the curve (AUC) of 0.69.

FIG. 8 shows a receiver operating characteristic curve for the PrevencioCAD panel FM152/757 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hadan area under the curve (AUC) of 0.73.

FIG. 9 shows a receiver operating characteristic curve for the PrevencioCAD panel FM117a/657 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.84.

FIG. 10 shows a receiver operating characteristic curve for thePrevencio CAD panel FM139CLa/658 in the internal validation set (N=278)to diagnose the presence of severe CAD (≥70% stenosis in any vessel).The panel had a robust area under the curve (AUC) of 0.80.

FIG. 11 shows a receiver operating characteristic curve for thePrevencio CAD panel FM139CLb/750 in the internal validation set (N=278)to diagnose the presence of severe CAD (≥70% stenosis in any vessel).The panel had a robust area under the curve (AUC) of 0.84.

FIG. 12 shows a receiver operating characteristic curve for thePrevencio CAD panel FM139CLc/751 in the internal validation set (N=278)to diagnose the presence of severe CAD (≥70% stenosis in any vessel).The panel had a robust area under the curve (AUC) of 0.83.

FIG. 13 shows a receiver operating characteristic curve for thePrevencio CAD panel FM117b/663 in the internal validation set (N=278) todiagnose the presence of severe CAD (≥70% stenosis in any vessel). Thepanel had a robust area under the curve (AUC) of 0.85.

FIG. 14 shows receiver operating characteristic curve for the PrevencioCAD panel FM139CLd/752 in the internal validation set (N=278) todiagnose the presence of severe CAD (≥70% stenosis in any vessel). Thepanel had a robust area under the curve (AUC) of 0.86.

FIG. 15 shows receiver operating characteristic curve for the PrevencioCAD panel FM139CLe/753 in the internal validation set (N=278) todiagnose the presence of severe CAD (≥70% stenosis in any vessel). Thepanel had a robust area under the curve (AUC) of 0.85.

FIG. 16 shows receiver operating characteristic curve for the PrevencioCAD panel FM139CLf/754 in the internal validation set (N=278) todiagnose the presence of severe CAD (≥70% stenosis in any vessel). Thepanel had a robust area under the curve (AUC) of 0.86.

FIG. 17 shows receiver operating characteristic curve for the PrevencioCAD panel FM139CLg/755 in the internal validation set (N=278) todiagnose the presence of severe CAD (≥70% stenosis in any vessel). Thepanel had a robust area under the curve (AUC) of 0.86.

FIG. 18 shows a receiver operating characteristic curve for thePrevencio CAD panel FM46/572 (as described in Example 2), in theinternal validation set (N=278) to diagnose the presence of severe CAD(≥70% stenosis in any vessel). The panel had a robust area under thecurve (AUC) of 0.84.

FIG. 19 shows a distribution of the CAD panel FM46/572 (as described inExample 2) in the internal validation set (N=278) to diagnose thepresence of severe CAD (≥70% stenosis in any vessel). A bimodaldistribution is noted, with preponderance of those with significant CADdistributed at higher scores. Positive=subjects with at least onecoronary stenosis ≥70%, negative=subjects with no coronary stenoses≥70%.

FIG. 20 shows Kaplan-Meier survival curves depicting time to incidentacute MI as a function of CAD Score panel FM 46/572 (as described inExample 2). Though developed as a diagnostic tool for CAD, the scorealso presaged incident acute MI during follow-up.

FIG. 21 shows receiver operating characteristic curve for the PrevencioCAD panel FM46Fd/586 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.84.

FIG. 22 shows receiver operating characteristic curve for the PrevencioCAD panel FM46Fe/587 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.83.

FIG. 23 shows receiver operating characteristic curve for the PrevencioCAD panel FM46Ff/588 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.80.

FIG. 24 shows receiver operating characteristic curve for the PrevencioCAD panel FM186/796 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.84.

FIG. 25 shows receiver operating characteristic curve for the PrevencioCAD panel FM189/798 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.83.

FIG. 26 shows receiver operating characteristic curve for the PrevencioCAD panel FM187/792 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.85.

FIG. 27 shows receiver operating characteristic curve for the PrevencioCAD panel FM188/794 in the internal validation set (N=278) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.85.

FIG. 28 shows receiver operating characteristic curve for the PrevencioCAD panel FM02/410 (as described Example 3), in the internal validationset (N=243) to diagnose the presence of severe CAD (≥70% stenosis in anyvessel). The panel had a robust area under the curve (AUC) of 0.89.

FIG. 29 shows receiver operating characteristic curve for the PrevencioCAD panel FM01/390 in the internal validation set (N=243) to diagnosethe presence of severe CAD (≥70% stenosis in any vessel). The panel hada robust area under the curve (AUC) of 0.87.

FIG. 30 shows receiver operating characteristic curve for the Prevencioprognostic panel FM160/02 (as described in Example 4), in the internalvalidation set (N=278) for prognosis of one year (3-365 day) compositecardiovascular death, myocardial infarct or stroke. The panel had anarea under the curve (AUC) of 0.79.

FIG. 31 shows receiver operating characteristic curve for the Prevencioprognostic panel FM96/04 (as described in Example 5) in the internalvalidation set (N=278) for prognosis of one year (0-365 day) compositecardiovascular death, myocardial infarct or stroke. The panel had anarea under the curve (AUC) of 0.77.

FIG. 32 shows receiver operating characteristic curve for the Prevencioprognostic panel FM190/33 in the internal validation set (N=278) forprognosis of one year (3-365 day) composite cardiovascular death,myocardial infarct or stroke. The panel had an area under the curve(AUC) of 0.78.

FIG. 33 shows receiver operating characteristic curve for the Prevencioprognostic panel FM98/03 in the internal validation set (N=278) forprognosis of one year (0-365 day) composite cardiovascular death,myocardial infarct or stroke. The panel had an area under the curve(AUC) of 0.75.

FIG. 34 shows receiver operating characteristic curve for the Prevencioprognostic panel FM209/02 in the internal validation set (N=278) forprognosis of one year (3-365 day) composite all-cause death, myocardialinfarct or stroke. The panel had an area under the curve (AUC) of 0.79.

FIG. 35 shows receiver operating characteristic curve for the Prevencioprognostic panel FM111/05 in the internal validation set (N=278) forprognosis of one year (0-365 day) composite all-cause death, myocardialinfarct or stroke. The panel had an area under the curve (AUC) of 0.77.

FIG. 36 shows receiver operating characteristic curve for the Prevencioprognostic panel FM210/03 in the internal validation set (N=278) forprognosis of one year (3-365 day) composite all-cause death, myocardialinfarct or stroke. The panel had an area under the curve (AUC) of 0.78.

FIG. 37 shows receiver operating characteristic curve for the Prevencioprognostic panel FM110/04 in the internal validation set (N=278) forprognosis of one year (0-365 day) composite all-cause death, myocardialinfarct or stroke. The panel had an area under the curve (AUC) of 0.75.

FIG. 38 shows receiver operating characteristic curve for the Prevencioprognostic panel FM211/03 in the internal validation set (N=278) forprognosis of one year (3-365 day) composite cardiovascular death ormyocardial infarct. The panel had an area under the curve (AUC) of 0.79.

FIG. 39 shows receiver operating characteristic curve for the Prevencioprognostic panel FM77/26 in the internal validation set (N=278) forprognosis of one year (0-365 day) composite cardiovascular death ormyocardial infarct. The panel had an area under the curve (AUC) of 0.77.

FIG. 40 shows receiver operating characteristic curve for the Prevencioprognostic panel FM212/02 in the internal validation set (N=278) forprognosis of one year (3-365 day) composite cardiovascular death ormyocardial infarct. The panel had an area under the curve (AUC) of 0.79.

FIG. 41 shows receiver operating characteristic curve for the Prevencioprognostic panel FM201/MI002 in the internal validation set (N=278) forprognosis of one year (3-365 day) myocardial infarct. The panel had anarea under the curve (AUC) of 0.76.

FIG. 42 shows receiver operating characteristic curve for the Prevencioprognostic panel FM204/MI003 in the internal validation set (N=278) forprognosis of one year (3-365 day) myocardial infarct. The panel had anarea under the curve (AUC) of 0.76.

FIG. 43 shows receiver operating characteristic curve for the Prevencioprognostic panel FM202/MI005 in the internal validation set (N=278) forprognosis of one year (3-365 day) myocardial infarct. The panel had anarea under the curve (AUC) of 0.75.

FIG. 44 shows receiver operating characteristic curve for the Prevencioprognostic panel FM205/MI007 in the internal validation set (N=278) forprognosis of one year (3-365 day) myocardial infarct. The panel had anarea under the curve (AUC) of 0.75.

FIG. 45 shows receiver operating characteristic curve for the Prevencioprognostic panel FM63/64 in the internal validation set (N=278) forprognosis of one year (0-365 day) myocardial infarct. The panel had anarea under the curve (AUC) of 0.73.

FIG. 46 shows receiver operating characteristic curve for the Prevencioprognostic panel FM52/244 in the internal validation set (N=278) forprognosis of one year (0-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.80.

FIG. 47 shows receiver operating characteristic curve for the Prevencioprognostic panel FM194/CVD001 in the internal validation set (N=278) forprognosis of one year (3-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.80.

FIG. 48 shows receiver operating characteristic curve for the Prevencioprognostic panel FM193/R08 in the internal validation set (N=278) forprognosis of one year (3-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.80.

FIG. 49 shows receiver operating characteristic curve for the Prevencioprognostic panel FM53/237 in the internal validation set (N=278) forprognosis of one year (0-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.81.

FIG. 50 shows receiver operating characteristic curve for the Prevencioprognostic panel FM195/CVD002 in the internal validation set (N=278) forprognosis of one year (3-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.81.

FIG. 51 shows receiver operating characteristic curve for the Prevencioprognostic panel FM207/04 in the internal validation set (N=278) forprognosis of one year (3-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.83.

FIG. 52 shows receiver operating characteristic curve for the Prevencioprognostic panel FM208/R05 in the internal validation set (N=278) forprognosis of one year (3-365 day) cardiovascular death. The panel had arobust area under the curve (AUC) of 0.82.

DETAILED DESCRIPTION OF THE INVENTION

The practice of the invention will employ, unless indicated specificallyto the contrary, conventional methods of chemistry, biochemistry,organic chemistry, molecular biology, microbiology, recombinant DNAtechniques, genetics, immunology, and cell biology that are within theskill of the art, many of which are described below for the purpose ofillustration. Such techniques are explained fully in the literature.See, e.g., Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rdEdition, 2001); Sambrook, et al., Molecular Cloning: A Laboratory Manual(2nd Edition, 1989); Maniatis et al., Molecular Cloning: A LaboratoryManual (1982); Ausubel et al., Current Protocols in Molecular Biology(John Wiley and Sons, updated July 2008); Short Protocols in MolecularBiology: A Compendium of Methods from Current Protocols in MolecularBiology, Greene Pub. Associates and Wiley-Interscience; Glover, DNACloning: A Practical Approach, vol. I & II (IRL Press, Oxford, 1985);Anand, Techniques for the Analysis of Complex Genomes, (Academic Press,New York, 1992); Transcription and Translation (B. Hames & S. Higgins,Eds., 1984); Perbal, A Practical Guide to Molecular Cloning (1984); andHarlow and Lane, Antibodies, (Cold Spring Harbor Laboratory Press, ColdSpring Harbor, N. Y., 1998).

All patents, patent applications, articles and publications mentionedherein, both supra and infra, are hereby expressly incorporated hereinby reference in their entireties.

Unless defined otherwise herein, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art to which this disclosure belongs. Various scientificdictionaries that include the terms included herein are well known andavailable to those in the art. Although any methods and materialssimilar or equivalent to those described herein find use in the practiceor testing of the disclosure, some preferred methods and materials aredescribed. Accordingly, the terms defined immediately below are morefully described by reference to the specification as a whole. It is tobe understood that this disclosure is not limited to the particularmethodology, protocols, and reagents described, as these may vary,depending upon the context in which they are used by those of skill inthe art.

As used herein, the singular terms “a”, “an”, and “the” include theplural reference unless the context clearly indicates otherwise.

The term “invention” or “present invention” as used herein is anon-limiting term and is not intended to refer to any single embodimentof the particular invention but encompasses all possible embodiments asdescribed in the specification and the claims.

Reference throughout this specification to, for example, “oneembodiment”, “an embodiment”, “another embodiment”, “a particularembodiment”, “a related embodiment”, “a certain embodiment”, “anadditional embodiment”, or “a further embodiment” or combinationsthereof means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, the appearances of theforegoing phrases in various places throughout this specification arenot necessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

As used herein, the term “about” or “approximately” refers to aquantity, level, value, number, frequency, percentage, dimension, size,amount, weight or length that varies by as much as 30, 25, 20, 25, 10,9, 8, 7, 6, 5, 4, 3, 2, or 1% to a reference quantity, level, value,number, frequency, percentage, dimension, size, amount, weight orlength. In particular embodiments, the terms “about” or “approximately”when preceding a numerical value indicates the value plus or minus arange of 15%, 10%, 5%, or 1%.

Throughout this specification, unless the context requires otherwise,the words “comprise”, “comprises” and “comprising” will be understood toimply the inclusion of a stated step or element or group of steps orelements but not the exclusion of any other step or element or group ofsteps or elements. By “consisting of” is meant including, and limitedto, whatever follows the phrase “consisting of.” Thus, the phrase“consisting of” indicates that the listed elements are required ormandatory, and that no other elements may be present. By “consistingessentially of” is meant including any elements listed after the phrase,and limited to other elements that do not interfere with or contributeto the activity or action specified in the disclosure for the listedelements. Thus, the phrase “consisting essentially of” indicates thatthe listed elements are required or mandatory, but that no otherelements are optional and may or may not be present depending uponwhether or not they affect the activity or action of the listedelements.

Cardiovascular Diseases and Events

The present disclosure relates generally to the diagnosis and/orprognosis of cardiovascular disease and/or adverse cardiovascularevents. As used herein, the term “diagnosis” refers to an identificationor likelihood of the presence of a cardiovascular disease or event in asubject. As also used herein, the term “prognosis” refers to thelikelihood or risk of a subject developing a particular outcome orparticular event.

The term “cardiovascular disease” refers to a class of diseases thatinvolve the heart or blood vessels. Cardiovascular disease includescoronary artery diseases (CAD), myocardial infarction (commonly known asa heart attack), stroke, hypertensive heart disease, rheumatic heartdisease, cardiomyopathy, cardiac arrhythmias (i.e., atrial fibrillation,ventricular tachycardia, etc.), cerebrovascular disease, peripheralarterial disease, and arterial thrombosis.

The term “cardiovascular event” as used herein denotes a variety ofadverse outcomes related to the cardiovascular system. These eventsinclude but are not limited to myocardial infarct, cardiovascular death,and stroke.

The term “coronary artery disease” or “CAD” refers to a particular typeof cardiovascular disease. “Obstructive coronary artery disease” ischaracterized by atherosclerotic obstruction in the coronary arteries.Such obstruction may be clinically relevant at levels of 50% or greater,60% or greater, 70% or greater, 80% or greater, 90% or greater, or 100%.Atherosclerotic plaque, the hallmark of atherosclerosis, progressivelynarrows the coronary artery lumen and impairs antegrade myocardial bloodflow. The reduction in coronary artery flow may be symptomatic orasymptomatic. Symptoms of coronary obstruction typically occur withexertion, but can occur at rest, and may culminate in a myocardialinfarction, stroke, and/or cardiovascular death depending on obstructionseverity and the rapidity of development.

The term “cardiovascular death” or “CV death” or “CVD” as used hereinrefers to death resulting from an acute myocardial infarction (MI),sudden cardiac death, death due to heart failure (HF), death due tostroke, death due to cardiovascular (CV) procedures, death due to CVthrombosis or hemorrhage, death due to cardiac infection, and death dueto other recognized CV causes within a specified period of time afterthe test sample is obtained.

The terms “myocardial infarction” or “MI” and “acute myocardialinfarction” or “AMI” are commonly known as a heart attack and occurswhen blood flow is limited to a part of the heart, causing damage to theheart muscle. The most common symptom is chest pain or discomfort whichmay travel into the shoulder, arm, back, neck, or jaw. Often, it is inthe center or left side of the chest and lasts for more than a fewminutes. The discomfort may occasionally feel like heartburn. Othersymptoms may include shortness of breath, nausea, feeling faint, a coldsweat, or feeling tired. Most MIs occur due to obstructive coronaryartery disease. Identified risk factors of obstructive coronary arterydisease include high blood pressure, smoking, diabetes, lack ofexercise, obesity, high blood cholesterol, poor diet, family history ofearly cardiovascular death or myocardial infarction, and excessivealcohol intake, among others. The mechanism of an MI often involvesprogression or rupture of the atherosclerotic plaque causing completeblockage of a coronary artery. MIs are less commonly caused by coronaryartery spasms, which may be due to medications such as cocaine,significant emotional stress, and extreme cold, among others.

The term “stroke” also known as cerebrovascular accident (CVA),cerebrovascular insult (CVI), or brain attack, refers to a situationwhen poor blood flow to the brain results in brain cell death. There aretwo main types of stroke: ischemic, due to lack of blood flow caused bythrombosis or embolus, and hemorrhagic, due to bleeding. They typicallyresult in significant dysfunction in the portions of the brain affected.Signs and symptoms of a stroke may include an inability to move or toexperience sensations of touch, heat, cold, or pain on one side of thebody, problems understanding or speaking, feeling like the world isspinning, or loss of vision to one side among others. Signs and symptomsoften appear soon after the stroke has occurred.

The term “all-cause death” or “all-cause mortality” is defined as deathfrom any non-trauma-related cause, including CV death, within aspecified period of time after the test sample is obtained.

Biomarkers and Clinical Variables

As described herein, biomarkers of the present invention can beadvantageously used in the diagnosis and prognosis of cardiovasculardiseases and events. The terms “marker” and “biomarker” are usedinterchangeably throughout the disclosure. As used herein, a biomarkerrefers generally to a protein or polypeptide, the level or concentrationof which is associated with a particular biological state, particularlya state associated with a cardiovascular disease, event or outcome.Panels, assays, kits and methods of the present invention may compriseantibodies, binding fragments thereof or other types of binding agents,which are specific for the biomarkers described herein.

The terms “polypeptide” and “protein”, used interchangeably herein,refer to a polymeric form of amino acids of any length, which caninclude coded and non-coded amino acids, chemically or biochemicallymodified or derivatized amino acids, and polypeptides having modifiedpeptide backbones. In various embodiments, detecting the levels ofnaturally occurring biomarker proteins in a biological sample iscontemplated for use within diagnostic, prognostic, or monitoringmethods disclosed herein. The term also includes fusion proteins,including, but not limited to, naturally occurring fusion proteins witha heterologous amino acid sequence, fusions with heterologous andhomologous leader sequences, with or without N-terminal methionineresidues; immunologically tagged proteins; and the like.

A “substantially isolated” or “isolated” substance is one that issubstantially free of its associated surrounding materials in nature. Bysubstantially free is meant at least 50%, preferably at least 70%, morepreferably at least 80%, and even more preferably at least 90% free ofthe materials with which it is associated in nature. As used herein,“isolated” can refer to polynucleotides, polypeptides, antibodies,cells, samples, and the like.

Certain illustrative diagnostic biomarkers of the present invention canbe found listed in Tables 1A and 1B, while certain illustrativeprognostic biomarkers can be found listed in Tables 2A and 2B. Based onthe information therein in Tables 1A, 1B, 2A, and 2B, the skilledartisan can readily identify, select and implement a biomarker orbiomarker combination in accordance with the present disclosure.

In certain specific embodiments, the biomarkers used in accordance withthe present invention include those listed in Tables 1A, 1B, 2A, and 2B,particularly those that are associated with a p-value of less than 0.1,less than 0.05, less than 0.01 or less than 0.001.

In other specific embodiments, the biomarkers used in accordance withthe present disclosure are selected from the group consisting ofadiponectin, apolipoprotein A-II, apolipoprotein C-I, decorin,interleukin-8, kidney injury molecule-1, matrix metalloproteinase 9,midkine, myoglobin, N terminal prohormone of brain natriuretic protein(NT-proBNP), osteopontin, pulmonary surfactant associated protein D,stem cell factor, tissue inhibitor of metalloproteinases-1 (TIMP-1),troponin, and vascular cell adhesion molecule (VCAM).

As used herein, “adiponectin” refers to a protein involved in regulatingglucose as well as fatty acid breakdown. It is also referred to asGBP-28, apM1, AdipoQ, and Acrp30. Adiponectin is a 244-amino-acidpeptide secreted by adipose tissue, whose roles include the regulationof glucose and fatty acid metabolism.

As used herein, “apolipoprotein A-II” refers to an apolipoprotein foundin high density lipoprotein (HDL) cholesterol in plasma.

As used herein, “apolipoprotein C-I” is a protein component oflipoproteins normally found in the plasma and responsible for theactivation of esterified lecithin cholesterol and in removal ofcholesterol from tissues.

As used herein, “decorin”, also known as PG40 and PGS2, is a protein,which belongs to the small leucine-rich proteoglycan family. Itregulates assembly of the extracellular collagen matrix.

As used herein, “interleukin-8”, also known as IL8, neutrophilchemotactic factor, chemokine ligand 8, and CXCL8, is a chemokineproduced by macrophages and other cell types such as epithelial cells,airway smooth muscle cells, and endothelial cells. It induces chemotaxisin target cells, primarily neutrophils but also other granulocytes,causing them to migrate toward the site of infection. IL-8 also inducesphagocytosis once they have arrived. IL-8 is also known to be a potentpromoter of angiogenesis. In target cells, IL-8 induces a series ofphysiological responses required for migration and phagocytosis, such asincreases in intracellular Ca²⁺, exocytosis (e.g. histamine release),and the respiratory burst.

As used herein, “kidney injury molecule-1”, also known as “KIM-1” is atype I cell membrane glycoprotein that serves as a receptor for oxidizedlipoproteins and plays a functional role in the kidney. KIM-1 is aproximal renal tubular marker whose concentrations have been linked toacute kidney injury.

As used herein, “matrix metalloproteinase 9”, also known as MMP-9, 92kDA type IV collagenase, 92 kDa gelatinase, and gelatinase B or GELB, isa is a matrixin, a class of enzymes that belong to thezinc-metalloproteinases family involved in the degradation of theextracellular matrix. Proteins of the matrix metalloproteinase (MMP)family are involved in the breakdown of extracellular matrix in normalphysiological processes, such as embryonic development, reproduction,angiogenesis, bone development, wound healing, cell migration, learningand memory, as well as in pathological processes, such as arthritis,intracerebral hemorrhage, and metastasis.

As used herein, “midkine”, also known as “neurite growth-promotingfactor 2” or “NEGF2”, refers to a basic heparin-binding growth factor oflow molecular weight and forms a family with pleiotrophin. Midkine is aheparin-binding cytokine/growth factor with a molecular weight of 13kDa.

As used herein, “myoglobin”, is an iron- and oxygen-binding proteinfound in the muscle tissue of vertebrates in general and in almost allmammals. Myoglobin is released from damaged muscle tissue(rhabdomyolysis), which has very high concentrations of myoglobin. Thereleased myoglobin is filtered by the kidneys but is toxic to the renaltubular epithelium and so may cause acute kidney injury. It is not themyoglobin itself that is toxic (it is a protoxin) but the ferrihemateportion that is dissociated from myoglobin in acidic environments (e.g.,acidic urine, lysosomes). Myoglobin is a sensitive marker for muscleinjury, making it a potential marker for heart attack in patients withchest pain.

As used herein, “N-terminal prohormone of brain natriuretic peptide” or“NT-PBNP” is also known as “NT-proBNP” or “BNPT” and refers to anN-terminal inactive protein that is cleaved from proBNP to release brainnatriuretic peptide.

As used herein, “osteopontin”, also known as “bone sialoprotein I”,“BSP-1”, “BNSP”, “early T-lymphocyte activation”, “ETA-1”, “secretedphosphoprotein 1”, “SPP1”, “2ar”, “Rickettsia resistance”, or “Ric”,refers to a glycoprotein (small integrin binding ligand N-linkedglycoprotein) first identified in osteoblasts. It includes all isoformsand post-translational modifications.

As used herein, “pulmonary surfactant associated protein D”, alsoreferred to as surfactant, pulmonary-associated protein D, or SP-D orSFTPD, is a protein that contributes to the lung's defense againstinhaled microorganisms, organic antigens and toxins.

As used herein, “stem cell factor”, also known as SCF, KIT-ligand, KL,and steel factor, is a cytokine that binds to the c-KIT receptor(CD117). SCF can exist both as a transmembrane protein and a solubleprotein. This cytokine plays an important role in hematopoiesis(formation of blood cells), spermatogenesis, and melanogenesis.

As used herein, “tissue inhibitor of metalloproteinases-1”, also knownas “TIMP-1” or “TIMP metallopeptidase inhibitor 1”, refers to aglycoprotein expressed in several tissues. It is a natural inhibitor ofmatrix metalloproteinases, a group of peptidases involved in thedegradation of extracellular matrix. It is able to promote cellproliferation in a wide range of cell types.

As used herein, “troponin”, also known as the troponin complex, is acomplex of three regulatory proteins (troponin C, troponin I, andtroponin T) that is integral to muscle contraction in skeletal muscleand cardiac muscle, but not smooth muscle. As used herein a troponinbiomarker may identify each of these proteins individually or incombination. An increased level of the cardiac protein isoform oftroponin circulating in the blood has been shown to be a biomarker ofheart disorders, the most important of which is myocardial infarction.Raised troponin levels indicate cardiac muscle cell death as themolecule is released into the blood upon injury to the heart.

As used herein, “vascular cell adhesion molecule”, also known as VCAM-1,VCAM, cluster of differentiation 106, and CD106, is a cell adhesionmolecule. The VCAM-1 protein mediates the adhesion of lymphocytes,monocytes, eosinophils, and basophils to vascular endothelium. It alsofunctions in leukocyte-endothelial cell signal transduction, and it mayplay a role in the development of atherosclerosis and rheumatoidarthritis.

It will be understood by one skilled in the art that these and otherbiomarkers disclosed herein (e.g., those set forth in Tables 1A, 1B, 2A,and 2B) can be readily identified, made and used in the context of thepresent disclosure in light of the information provided herein.

As used herein, the term “score” refers to a binary, multilevel, orcontinuous result as it relates diagnostic or prognostic determinations.

As used herein, the term “panel” refers to specific combination ofbiomarkers and clinical markers used to determine a diagnosis orprognosis of a cardiovascular disease or outcome in a subject. The term“panel” may also refer to an assay comprising a set of biomarkers usedto determine a diagnosis or prognosis of a cardiovascular disease oroutcome in a subject.

As further described herein, the “training set” is the set of patientsor patient samples that are used in the process of training (i.e.,developing, evaluating and building) the final diagnostic or prognosticmodel. The “validation set” is a set of patients or patient samples thatare withheld from the training process, and are only used to validatethe performance of the final diagnostic or prognostic model.

As further described herein, the biomarkers of the present invention canoptionally be used in combination with certain clinical variables inorder to provide for an improved diagnosis and/or prognosis of acardiovascular disease or event in a subject. For example, illustrativeclinical variables useful in the context of the present disclosure canbe found listed in Tables 3A, 3B, 4A, and 4B.

Table 1A below shows biomarker concentrations and their diagnosticassociation that differ between those in the training set (N=649) withat least one coronary artery stenosis ≥70% (N=428) and those who did notin the cohort of subjects who received a coronary cath, with or withoutan optional peripheral cath.

TABLE 1A Diagnostic Biomarkers (Received Coronary Cath; Peripheral CathOptional) (Training Set) Concentration in Concentration in Subjects withSubjects without Coronary Stenosis Coronary Stenosis Biomarker (N = 428)(N = 221) p-value Adiponectin (ug/mL) 3.4 (2.2, 5.1) 4.5 (2.9, 7.2)<0.001 Alpha-1-Antitrypsin (AAT) 1.8 (1.5, 2.1) 1.8 (1.5, 2.1) 0.248(mg/mL) Alpha-2-Macroglobulin 1.9 (1.6, 2.3) 1.9 (1.6, 2.3) 0.816(A2Macro) (mg/mL) Angiopoietin-1 (ANG-1) 6.7 (5, 9.6) 7.3 (5, 11) 0.179(ng/mL) Angiotensin-Converting 79 (60, 103.2) 78 (59.8, 105) 0.954Enzyme (ACE) (ng/mL) Apolipoprotein(a) (Lp(a)) 193.5 (68, 457.2) 152(56, 446.5) 0.295 (ug/mL) Apolipoprotein A-I (Apo A-I) 1.7 (1.4, 2.1)1.8 (1.6, 2.2) <0.001 (mg/mL) Apolipoprotein A-II (Apo A-II) 309 (247,371.2) 308 (255, 376.2) 0.662 (ng/mL) Apolipoprotein B (Apo B) 1350(1040, 1790) 1390 (1150, 1852) 0.113 (ug/mL) Apolipoprotein C-I (ApoC-I) 307 (252, 367.8) 336.5 (277.8, 391.2) <0.001 (ng/mL) ApolipoproteinC-III (Apo C-III) 218 (164, 271.8) 212 (155.8, 267.2) 0.691 (ug/mL)Apolipoprotein H (Apo H) 331 (270, 390.5) 343.5 (268, 384) 0.987 (ug/mL)Beta-2-Microglobulin (B2M) 1.7 (1.4, 2.5) 1.6 (1.4, 2.1) 0.012 (ug/mL)Brain-Derived Neurotrophic 2.15 (0.9, 4.325) 2.75 (1.1, 4.725) 0.017Factor (BDNF) (ng/mL) C-Reactive Protein (CRP) 3.75 (1.6, 11) 3.05 (1.2,7.575) 0.014 (ug/mL) Carbonic anhydrase 9 (CA-9) 0.16 (0.091, 0.26) 0.14(0.085, 0.222) 0.055 (ng/mL) Carcinoembryonic antigen- 24 (20, 27) 23(21, 28.2) 0.258 related cell adhesion molecule 1 (CEACAM1) (ng/mL) CD5Antigen-like (CD5L) 3760 (2898, 5275) 3470 (2690, 4900) 0.031 (ng/mL)Decorin (ng/mL) 2.4 (2, 3.6) 2.3 (1.9, 2.9) 0.009 E-Selectin (ng/mL) 5.2(3.6, 7.1) 4.8 (3.6, 6.8) 0.312 EN-RAGE (ng/mL) 28 (16, 50) 24 (15.8,49) 0.243 Eotaxin-1 (pg/mL) 104 (42.5, 148) 96 (42.5, 137.2) 0.343Factor VII (ng/mL) 465.5 (346, 587.2) 451.5 (359.5, 577.8) 0.894Ferritin (FRTN) (ng/mL) 134 (69.8, 235) 129.5 (67, 198.8) 0.409 Fetuin-A(ug/mL) 698.5 (582.5, 828) 675.5 (583.8, 810) 0.627 Fibrinogen (mg/mL)4.4 (3.6, 5.4) 4.1 (3.4, 5.1) 0.026 Follicle-Stimulating Hormone 6.2(3.7, 17) 8.7 (3.5, 42.2) 0.011 (FSH) (mIU/mL) Growth Hormone (GH) 0.32(0.07, 0.9) 0.26 (0.07, 0.69) 0.134 (ng/mL) Haptoglobin (mg/mL) 1.3(0.66, 2.1) 0.88 (0.478, 1.7) <0.001 Immunoglobulin A (IgA) 2.4 (1.5,3.425) 2.25 (1.6, 3.125) 0.536 (mg/mL) Immunoglobulin M (IgM) 1.4(0.928, 2.2) 1.4 (0.995, 2.225) 0.348 (mg/mL) Insulin (uIU/mL) 1 (0.11,2.5) 0.49 (0.11, 1.5) <0.001 Intercellular Adhesion 107 (85, 133) 102.5(83, 125.2) 0.102 Molecule 1 (ICAM-1) (ng/mL) Interferon gamma Induced307.5 (232.8, 399.2) 288 (223.2, 402) 0.207 Protein 10 (IP-10) (pg/mL)Interleukin-1 receptor 119 (90, 158) 108.5 (83.8, 140.2) 0.005antagonist (IL-1ra) (pg/mL) Interleukin-6 receptor (IL-6r) 24 (19, 29)23 (18, 29) 0.174 (ng/mL) Interleukin-8 (IL-8) (pg/mL) 6.7 (4.6, 10) 5.7(4, 9) 0.01 Interleukin-12 Subunit p40 0.595 (0.468, 0.73) 0.57 (0.44,0.71) 0.132 (IL-12p40) (ng/mL) Interleukin-15 (IL-15) (ng/mL) 0.57(0.46, 0.7) 0.54 (0.448, 0.66) 0.071 Interleukin-18 (IL-18) (pg/mL) 203(155.2, 272) 188 (135.5, 255) 0.009 Interleukin-18-binding protein 9.6(7.4, 13) 8.8 (6.6, 11) <0.001 (IL-18bp) (ng/mL) Interleukin-23 (IL-23)(ng/mL) 2.6 (2, 3.2) 2.4 (1.9, 3.1) 0.146 Kidney Injury Molecule-1 0.043(0.014, 0.073) 0.032 (0.014, 0.052) <0.001 (KIM-1) (ng/mL) Leptin(ng/mL) 9.2 (4.5, 21) 7.9 (4, 20) 0.424 Luteinizing Hormone (LH) 4.7(3.3, 7.9) 5.3 (3.375, 13) 0.014 (mIU/mL) Macrophage Colony- 0.45 (0.16,0.73) 0.38 (0.16, 0.572) 0.005 Stimulating Factor 1 (M-CSF) (ng/mL)Macrophage Inflammatory 258 (193.8, 355.2) 264 (182.5, 351) 0.539Protein-1 beta (MIP-1 beta) (pg/mL) Matrix Metalloproteinase-2 1360(1130, 1642) 1320 (1120, 1600) 0.314 (MMP-2) (ng/mL) MatrixMetalloproteinase-3 7.2 (5.3, 11) 6 (4.3, 9.2) <0.001 (MMP-3) (ng/mL)Matrix Metalloproteinase-7 0.37 (0.26, 0.58) 0.3 (0.218, 0.46) <0.001(MMP-7) (ng/mL) Matrix Metalloproteinase-9 128 (91.5, 183) 119.5 (86.8,167.2) 0.197 (MMP-9) (ng/mL) Matrix Metalloproteinase-9, 597.5 (435.5,833.2) 531 (379.5, 741) 0.013 total (MMP-9, total) (ng/mL) Midkine(ng/mL) 15 (10.8, 22) 12 (9.9, 17) <0.001 Monocyte Chemotactic 112 (79,160.2) 103 (77, 152) 0.272 Protein 1 (MCP-1) (pg/mL) MonocyteChemotactic 23 (17, 29) 23 (17.8, 30) 0.804 Protein 2 (MCP-2) (pg/mL)Monocyte Chemotactic 2300 (1720, 3382) 2300 (1538, 3362) 0.587 Protein 4(MCP-4) (pg/mL) Monokine Induced by Gamma 990 (591.5, 1780) 852 (551,1462) 0.033 Interferon (MIG) (pg/mL) Myeloid Progenitor Inhibitory 1.3(0.98, 1.6) 1.1 (0.88, 1.4) <0.001 Factor 1 (MPIF-1) (ng/mL) Myoglobin(ng/mL) 33 (24, 52.2) 28 (20, 43.2) <0.001 N-terminal prohormone of 1520(552.5, 4270) 1370 (449.8, 3650) 0.144 brain natriuretic peptide (NTproBNP) (pg/mL) Osteopontin (ng/mL) 28 (21, 43.5) 26 (19, 37) 0.022Pancreatic Polypeptide (PPP) 98 (54, 183) 79 (43, 130) 0.005 (pg/mL)Plasminogen Activator 43 (26, 69) 46.5 (25, 75) 0.465 Inhibitor 1(PAI-1) (ng/mL) Platelet endothelial cell 54 (46, 64.2) 55 (45, 62.2)0.575 adhesion molecule (PECAM-1) (ng/mL) Prolactin (PRL) (ng/mL) 8(5.4, 12) 8.4 (5.6, 13) 0.188 Pulmonary and Activation- 101 (75.8,138.2) 92 (65.8, 135.2) 0.08 Regulated Chemokine (PARC) (ng/mL)Pulmonary surfactant- 5.5 (3.4, 8.7) 4.5 (3.1, 7.3) 0.003 associatedprotein D (SP-D) (ng/mL) Resistin (ng/mL) 2.4 (1.8, 3.5) 2.3 (1.7, 3.2)0.149 Serotransferrin (Transferrin) 273.5 (235.8, 316.2) 274.5 (233,315) 0.765 (mg/dl) Serum Amyloid P-Component 13 (10, 16) 12 (9.4, 15)0.016 (SAP) (ug/mL) Stem Cell Factor (SCF) (pg/mL) 376 (292, 478.2)340.5 (258, 423.2) <0.001 T-Cell-Specific Protein RANTES 8.1 (3.7, 16)9.3 (4.5, 19) 0.07 (RANTES) (ng/mL) Tamm-Horsfall Urinary 0.029 (0.02,0.038) 0.034 (0.024, 0.044) <0.001 Glycoprotein (THP) (ug/mL)Thrombomodulin (TM) 3.8 (3.1, 4.7) 3.55 (3, 4.2) 0.002 (ng/mL)Thrombospondin-1 (ng/mL) 4090 (2020, 7100) 5260 (2442, 7742) 0.019Thyroid-Stimulating Hormone 1.2 (0.79, 1.8) 1.2 (0.82, 1.8) 0.385 (TSH)(uIU/mL) Thyroxine-Binding Globulin 38 (32, 44) 36 (29, 45) 0.124 (TBG)(ug/mL) Tissue Inhibitor of 73 (60, 94) 72.5 (58, 90.2) 0.451Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin (TTR) (mg/dl) 26(22, 30) 25.5 (21, 31) 0.854 Troponin (pg/ml) 9.6 (4, 35.7) 5.8 (3,13.6) <0.001 Tumor necrosis factor 6.4 (4.8, 9.6) 6 (4.5, 7.5) 0.001receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 586 (464.8, 730.2) 528(442, 669.2) 0.004 Molecule-1 (VCAM-1) (ng/mL) Vascular EndothelialGrowth 98.5 (70.8, 137.5) 103.5 (73.8, 140) 0.373 Factor (VEGF) (pg/mL)Vitamin D-Binding Protein 249 (191.2, 310.5) 249 (194.5, 306.2) 0.927(VDBP) (ug/mL) Vitamin K-Dependent Protein 14 (11, 17) 13 (11, 16) 0.078S (VKDPS) (ug/mL) Vitronectin (ug/mL) 465 (352, 593) 444.5 (349.5, 552)0.148 von Willebrand Factor (vWF) 134 (96, 181) 124.5 (90, 175.2) 0.147(ug/mL)

Table 1B below shows biomarker concentrations and their diagnosticassociation that differ between those in the training set (N=566) withat least one coronary artery stenosis ≥70% (N=361) and those who did notin the cohort of subjects who received a coronary cath only.

TABLE 1B Diagnostic Biomarkers (Received Coronary Cath Only) (TrainingSet) Concentration in Concentration in Subjects with Subjects withoutCoronary Stenosis Coronary Stenosis Biomarker (N = 361) (N = 205)p-value Adiponectin (ug/mL) 3.5 (2.2, 5.4) 4.6 (2.9, 7.3) <0.001Alpha-1-Antitrypsin (AAT) 1.8 (1.5, 2.1) 1.8 (1.5, 2.1) 0.479 (mg/mL)Alpha-2-Macroglobulin 1.9 (1.5, 2.3) 1.9 (1.6, 2.3) 0.989 (A2Macro)(mg/mL) Angiopoietin-1 (ANG-1) 6.7 (5, 9.5) 7.3 (5.1, 11) 0.155 (ng/mL)Angiotensin-Converting 79 (60, 106) 77.5 (59.8, 103.2) 0.763 Enzyme(ACE) (ng/mL) Apolipoprotein(a) (Lp(a)) 199 (69, 455) 156.5 (55.5,446.5) 0.313 (ug/mL) Apolipoprotein A-I (Apo A-I) 1.7 (1.4, 2.1) 1.9(1.6, 2.225) <0.001 (mg/mL) Apolipoprotein A-II (Apo A-II) 308 (247,369) 311.5 (255, 377.2) 0.5 (ng/mL) Apolipoprotein B (Apo B) 1320 (1040,1770) 1395 (1150, 1875) 0.048 (ug/mL) Apolipoprotein C-I (Apo C-I) 308(251, 367) 339 (287.8, 393.5) <0.001 (ng/mL) Apolipoprotein C-III (ApoC-III) 218 (159, 274) 214 (158, 267.2) 0.977 (ug/mL) Apolipoprotein H(Apo H) 328 (270, 388) 344.5 (277.2, 384.2) 0.707 (ug/mL)Beta-2-Microglobulin (B2M) 1.7 (1.3, 2.4) 1.6 (1.4, 2.1) 0.096 (ug/mL)Brain-Derived Neurotrophic 2.2 (0.89, 4.3) 2.85 (1.175, 4.7) 0.013Factor (BDNF) (ng/mL) C-Reactive Protein (CRP) 3.5 (1.5, 10) 3.2 (1.3,8) 0.094 (ug/mL) Carbonic anhydrase 9 (CA-9) 0.16 (0.09, 0.26) 0.14(0.084, 0.22) 0.04 (ng/mL) Carcinoembryonic antigen- 23 (20, 27) 23 (21,28) 0.271 related cell adhesion molecule 1 (CEACAM1) (ng/mL) CD5Antigen-like (CD5L) 3750 (2840, 5110) 3470 (2690, 4845) 0.101 (ng/mL)Decorin (ng/mL) 2.4 (2, 3.7) 2.3 (1.9, 2.925) 0.014 E-Selectin (ng/mL)5.2 (3.6, 7) 4.8 (3.6, 6.8) 0.33 EN-RAGE (ng/mL) 27 (16, 51) 25 (15, 49)0.333 Eotaxin-1 (pg/mL) 102 (42.5, 144) 96 (42.5, 137) 0.664 Factor VII(ng/mL) 465 (340, 592) 450 (357.8, 577) 0.971 Ferritin (FRTN) (ng/mL)137 (73, 241) 130 (67, 197.2) 0.303 Fetuin-A (ug/mL) 700 (584, 829)676.5 (583.8, 13.8) 0.689 Fibrinogen (mg/mL) 4.4 (3.6, 5.3) 4.1 (3.5,5.1) 0.133 Follicle-Stimulating Hormone 6 (3.7, 17) 8.8 (3.6, 43) 0.007(FSH) (mIU/mL) Growth Hormone (GH) 0.34 (0.07, 0.98) 0.26 (0.07, 0.69)0.053 (ng/mL) Haptoglobin (mg/mL) 1.3 (0.65, 2.1) 0.825 (0.458, 1.6)<0.001 Immunoglobulin A (IgA) 2.4 (1.5, 3.4) 2.2 (1.5, 3.125) 0.445(mg/mL) Immunoglobulin M (IgM) 1.4 (0.93, 2.2) 1.4 (1, 2.3) 0.34 (mg/mL)Insulin (uIU/mL) 1 (0.22, 2.5) 0.545 (0.11, 1.6) <0.001 IntercellularAdhesion 106 (85, 133) 102 (81.8, 125) 0.098 Molecule 1 (ICAM-1) (ng/mL)Interferon gamma Induced 304 (232, 396) 291 (227.8, 406.2) 0.631 Protein10 (IP-10) (pg/mL) Interleukin-1 receptor 119 (90, 158) 108.5 (84, 141)0.018 antagonist (IL-1ra) (pg/mL) Interleukin-6 receptor (IL-6r) 24 (19,29) 23 (18, 29) 0.304 (ng/mL) Interleukin-8 (IL-8) (pg/mL) 6.6 (4.6, 10)5.7 (4, 8.8) 0.014 Interleukin-12 Subunit p40 0.58 (0.46, 0.73) 0.57(0.44, 0.71) 0.346 (IL-12p40) (ng/mL) Interleukin-15 (IL-15) (ng/mL)0.57 (0.45, 0.7) 0.54 (0.45, 0.67) 0.231 Interleukin-18 (IL-18) (pg/mL)204 (156, 272) 188 (136, 256) 0.021 Interleukin-18-binding protein 9.4(7.3, 13) 8.9 (6.6, 11) 0.002 (IL-18bp) (ng/mL) Interleukin-23 (IL-23)(ng/mL) 2.6 (2, 3.3) 2.4 (1.9, 3.1) 0.253 Kidney Injury Molecule-1 0.042(0.014, 0.07) 0.032 (0.014, 0.051) <0.001 (KIM-1) (ng/mL) Leptin (ng/mL)9.2 (4.3, 21) 8.1 (4, 20.2) 0.603 Luteinizing Hormone (LH) 4.7 (3.3,7.8) 5.35 (3.375, 13) 0.01 (mIU/mL) Macrophage Colony- 0.45 (0.16, 0.73)0.38 (0.16, 0.572) 0.012 Stimulating Factor 1 (M-CSF) (ng/mL) MacrophageInflammatory 256 (191, 345) 269 (187.5, 350) 0.964 Protein-1 beta (MIP-1beta) (pg/mL) Matrix Metalloproteinase-2 1360 (1130, 1640) 1320 (1120,1615) 0.35 (MMP-2) (ng/mL) Matrix Metalloproteinase-3 7 (5.1, 11) 6(4.3, 9.2) 0.001 (MMP-3) (ng/mL) Matrix Metalloproteinase-7 0.36 (0.25,0.56) 0.305 (0.21, 0.46) 0.006 (MMP-7) (ng/mL) MatrixMetalloproteinase-9 133 (94, 183) 119.5 (89.2, 168.5) 0.147 (MMP-9)(ng/mL) Matrix Metalloproteinase-9, 605 (440, 859) 531 (379.5, 741.5)0.007 total (MMP-9, total) (ng/mL) Midkine (ng/mL) 14 (10, 21) 12 (9.8,17) 0.004 Monocyte Chemotactic 110 (77, 158) 103 (78, 150.5) 0.567Protein 1 (MCP-1) (pg/mL) Monocyte Chemotactic 23 (17, 29) 23 (18, 30)0.297 Protein 2 (MCP-2) (pg/mL) Monocyte Chemotactic 2260 (1690, 3390)2305 (1562, 3360) 0.85 Protein 4 (MCP-4) (pg/mL) Monokine Induced byGamma 964 (578, 1750) 877.5 (551, 1560) 0.175 Interferon (MIG) (pg/mL)Myeloid Progenitor Inhibitory 1.3 (0.97, 1.6) 1.1 (0.88, 1.5) 0.003Factor 1 (MPIF-1) (ng/mL) Myoglobin (ng/mL) 33 (24, 52) 27 (20, 43.2)<0.001 N-terminal prohormone of 1500 (535, 4700) 1380 (449.8, 3820) 0.15brain natriuretic peptide (NT proBNP) (pg/mL) Osteopontin (ng/mL) 28(20, 43.2) 25 (18.5, 35.5) 0.049 Pancreatic Polypeptide (PPP) 93 (49,181) 76.5 (43, 131) 0.02 (pg/mL) Plasminogen Activator 44 (26, 69) 47(25.8, 75) 0.442 Inhibitor 1 (PAI-1) (ng/mL) Platelet endothelial cell54 (45, 64) 55 (45, 62) 0.702 adhesion molecule (PECAM-1) (ng/mL)Prolactin (PRL) (ng/mL) 8 (5.2, 12) 8.4 (5.5, 13) 0.195 Pulmonary andActivation- 99 (74, 135) 94 (66, 136.2) 0.329 Regulated Chemokine (PARC)(ng/mL) Pulmonary surfactant- 5.5 (3.5, 8.7) 4.5 (3, 7.2) 0.002associated protein D (SP-D) (ng/mL) Resistin (ng/mL) 2.4 (1.8, 3.5) 2.3(1.7, 3.2) 0.268 Serotransferrin (Transferrin) 272 (235, 314) 276.5(233, 315) 0.994 (mg/dl) Serum Amyloid P-Component 13 (10, 16) 12 (9.4,15) 0.052 (SAP) (ug/mL) Stem Cell Factor (SCF) (pg/mL) 374 (284, 478)341 (258, 423.2) 0.006 T-Cell-Specific Protein RANTES 8 (3.6, 16) 9.5(4.4, 18) 0.061 (RANTES) (ng/mL) Tamm-Horsfall Urinary 0.03 (0.02,0.039) 0.034 (0.024, 0.044) 0.002 Glycoprotein (THP) (ug/mL)Thrombomodulin (TM) 3.8 (3.1, 4.7) 3.55 (3, 4.2) 0.01 (ng/mL)Thrombospondin-1 (ng/mL) 3940 (2020, 7080) 5360 (2478, 7655) 0.011Thyroid-Stimulating Hormone 1.1 (0.79, 1.7) 1.2 (0.818, 1.8) 0.218 (TSH)(uIU/mL) Thyroxine-Binding Globulin 37 (32, 44) 36 (29, 45) 0.359 (TBG)(ug/mL) Tissue Inhibitor of 72 (58, 93) 71.5 (58, 89.5) 0.712Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin (TTR) (mg/dl) 26(22, 30) 26 (21, 31) 0.928 Troponin (pg/ml) 9.6 (3.8, 40.7) 5.8 (2.9,14) <0.001 Tumor necrosis factor 6.3 (4.8, 9.6) 6 (4.6, 7.5) 0.015receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 588 (465, 733) 533(448.8, 682.2) 0.022 Molecule-1 (VCAM-1) (ng/mL) Vascular EndothelialGrowth 99 (68, 144) 105.5 (73.8, 140) 0.39 Factor (VEGF) (pg/mL) VitaminD-Binding Protein 248 (188, 313) 250 (192.2, 310.2) 0.984 (VDBP) (ug/mL)Vitamin K-Dependent Protein 14 (11, 17) 13 (11, 16) 0.099 S (VKDPS)(ug/mL) Vitronectin (ug/mL) 467 (352, 593) 446 (350, 552) 0.224 vonWillebrand Factor (vWF) 135 (95, 184) 124.5 (90.8, 171.8) 0.187 (ug/mL)

Table 2A shows biomarker concentrations and their prognostic associationin patients with major adverse cardiac events (MACE) within 365 days ofthe blood draw (for the training set, N=649). The numbers in this tablewere calculated using the composite endpoint of one-year MACE with CVdeath, MI, or major stroke; these proteins produce similar results withthe composite endpoint of one-year MACE with all-cause death, MI and/ormajor stroke.

TABLE 2A Prognostic Biomarkers (Within 365 Days Post-Cath) (TrainingSet) Concentration in Concentration in Subjects with Subjects withoutone-year MACE one-year MACE Biomarker (N = 82) (N = 567) p-valueAdiponectin (ug/mL) 4.9 (2.85, 7.45) 3.6 (2.325, 5.6) 0.002Alpha-1-Antitrypsin (AAT) 2 (1.625, 2.4) 1.8 (1.5, 2.1) <0.001 (mg/mL)Alpha-2-Macroglobulin 2.1 (1.7, 2.575) 1.9 (1.6, 2.3) 0.001 (A2Macro)(mg/mL) Angiopoietin-1 (ANG-1) 7 (5, 10.8) 6.8 (5, 9.8) 0.715 (ng/mL)Angiotensin-Converting 75 (59, 106.5) 79 (60, 104.8) 0.629 Enzyme (ACE)(ng/mL) Apolipoprotein(a) (Lp(a)) 187 (77.8, 438.5) 169.5 (60.2, 454.8)0.619 (ug/mL) Apolipoprotein A-I (Apo A-I) 1.75 (1.4, 2) 1.8 (1.5, 2.1)0.117 (mg/mL) Apolipoprotein A-II (Apo A-II) 262 (214, 333.5) 312.5(255.5, 380) <0.001 (ng/mL) Apolipoprotein B (Apo B) 1270 (964.5, 1668)1390 (1090, 1820) 0.028 (ug/mL) Apolipoprotein C-I (Apo C-I) 309 (245.2,357.2) 314 (262, 381.5) 0.089 (ng/mL) Apolipoprotein C-III (Apo C-III)222 (170.5, 274) 215.5 (158.2, 271) 0.399 (ug/mL) Apolipoprotein H (ApoH) 328 (271.2, 388.5) 333 (270, 387.8) 0.965 (ug/mL)Beta-2-Microglobulin (B2M) 2.5 (1.8, 3.425) 1.6 (1.3, 2.2) <0.001(ug/mL) Brain-Derived Neurotrophic 2 (0.755, 4.325) 2.35 (1, 4.6) 0.238Factor (BDNF) (ng/mL) C-Reactive Protein (CRP) 6.55 (2.225, 18.75) 3.3(1.4, 8.3) <0.001 (ug/mL) Carbonic anhydrase 9 (CA-9) 0.23 (0.15, 0.338)0.14 (0.085, 0.238) <0.001 (ng/mL) Carcinoembryonic antigen- 24 (21, 29)23 (20, 27.8) 0.254 related cell adhesion molecule 1 (CEACAM1) (ng/mL)CD5 Antigen-like (CD5L) 4200 (2798, 6418) 3700 (2792, 4998) 0.058(ng/mL) Decorin (ng/mL) 2.85 (2.1, 4.075) 2.3 (1.9, 3.1) <0.001E-Selectin (ng/mL) 4.8 (3.5, 7) 5.1 (3.7, 7) 0.436 EN-RAGE (ng/mL) 34.5(15, 56.8) 26 (16, 49) 0.378 Eotaxin-1 (pg/mL) 118.5 (42.5, 153) 97(42.5, 143.8) 0.034 Factor VII (ng/mL) 404 (294.8, 544) 468 (366, 586.5)0.009 Ferritin (FRTN) (ng/mL) 136 (81.2, 249.2) 132 (68, 217.8) 0.17Fetuin-A (ug/mL) 644.5 (539.8, 766) 696 (593, 828.5) 0.005 Fibrinogen(mg/mL) 4.9 (4, 5.6) 4.2 (3.5, 5.3) 0.003 Follicle-Stimulating Hormone7.7 (4, 34.5) 6.3 (3.6, 26.8) 0.513 (FSH) (mIU/mL) Growth Hormone (GH)0.55 (0.19, 1.075) 0.28 (0.07, 0.798) 0.009 (ng/mL) Haptoglobin (mg/mL)1.2 (0.68, 2.2) 1.1 (0.54, 1.9) 0.104 Immunoglobulin A (IgA) 2.25 (1.6,3.2) 2.4 (1.5, 3.4) 0.959 (mg/mL) Immunoglobulin M (IgM) 1.4 (1.1, 2)1.4 (0.942, 2.2) 0.674 (mg/mL) Insulin (uIU/mL) 0.89 (0.11, 1.8) 0.815(0.11, 2.175) 0.837 Intercellular Adhesion 107 (86, 132.5) 104 (84, 131)0.557 Molecule 1 (ICAM-1) (ng/mL) Interferon gamma Induced 335 (242.5,432.5) 299.5 (228, 398) 0.094 Protein 10 (IP-10) (pg/mL) Interleukin-1receptor 122 (97.2, 160.5) 114 (88, 148) 0.1 antagonist (IL-1ra) (pg/mL)Interleukin-6 receptor (IL-6r) 23 (19, 29.8) 24 (19, 29) 0.988 (ng/mL)Interleukin-8 (IL-8) (pg/mL) 10 (6.7, 15.8) 6 (4.2, 9) <0.001Interleukin-12 Subunit p40 (IL- 0.64 (0.48, 0.758) 0.58 (0.45, 0.71)0.186 12p40) (ng/mL) Interleukin-15 (IL-15) (ng/mL) 0.56 (0.46, 0.69)0.555 (0.45, 0.7) 0.712 Interleukin-18 (IL-18) (pg/mL) 202 (154.8,288.8) 198 (144, 266) 0.33 Interleukin-18-binding protein 13 (9.3, 19)8.9 (7, 12) <0.001 (IL-18bp) (ng/mL) Interleukin-23 (IL-23) (ng/mL) 2.5(1.725, 3.275) 2.5 (2, 3.2) 0.508 Kidney Injury Molecule-1 0.062 (0.042,0.14) 0.034 (0.014, 0.058) <0.001 (KIM-1) (ng/mL) Leptin (ng/mL) 7.8(3.5, 18.8) 9.1 (4.2, 21) 0.252 Luteinizing Hormone (LH) 5.35 (3.5,11.5) 4.75 (3.3, 8.775) 0.168 (mIU/mL) Macrophage Colony- 0.74 (0.482,1.4) 0.385 (0.16, 0.6) <0.001 Stimulating Factor 1 (M-CSF) (ng/mL)Macrophage Inflammatory 270 (196.2, 401.5) 258 (188, 345) 0.414Protein-1 beta (MIP-1 beta) (pg/mL) Matrix Metalloproteinase-2 1440(1255, 1888) 1325 (1120, 1608) <0.001 (MMP-2) (ng/mL) MatrixMetalloproteinase-3 9.6 (7, 15.8) 6.6 (4.7, 9.8) <0.001 (MMP-3) (ng/mL)Matrix Metalloproteinase-7 0.46 (0.33, 0.768) 0.34 (0.232, 0.51) <0.001(MMP-7) (ng/mL) Matrix Metalloproteinase-9 123.5 (76, 192.8) 126.5(91.2, 177.8) 0.608 (MMP-9) (ng/mL) Matrix Metalloproteinase-9, 554.5(382.5, 931) 580.5 (419.2, 795) 0.921 total (MMP-9, total) (ng/mL)Midkine (ng/mL) 21.5 (13.2, 36.5) 13 (10, 19) <0.001 MonocyteChemotactic 113.5 (74, 161) 108 (78, 158) 0.811 Protein 1 (MCP-1)(pg/mL) Monocyte Chemotactic 24 (19, 29.8) 23 (17, 30) 0.268 Protein 2(MCP-2) (pg/mL) Monocyte Chemotactic 2295 (1680, 3385) 2300 (1662, 3360)0.867 Protein 4 (MCP-4) (pg/mL) Monokine Induced by Gamma 1555 (876.8,2538) 876 (554.5, 1588) <0.001 Interferon (MIG) (pg/mL) MyeloidProgenitor Inhibitory 1.5 (1.1, 2) 1.2 (0.93, 1.5) <0.001 Factor 1(MPIF-1) (ng/mL) Myoglobin (ng/mL) 47 (29, 78.5) 30 (21, 45.8) <0.001N-terminal prohormone of 5610 (2050, 15980) 1310 (460.8, 3235) <0.001brain natriuretic peptide (NT proBNP) (pg/mL) Osteopontin (ng/mL) 49(26.2, 82.5) 26 (19, 37) <0.001 Pancreatic Polypeptide (PPP) 147 (65,317) 87 (47.2, 148) <0.001 (pg/mL) Plasminogen Activator 46 (26, 74.2)44 (26, 71.8) 0.905 Inhibitor 1 (PAI-1) (ng/mL) Platelet endothelialcell 55 (46, 67.5) 54 (45, 63) 0.344 adhesion molecule (PECAM-1) (ng/mL)Prolactin (PRL) (ng/mL) 9.8 (6.2, 15) 7.9 (5.4, 12) 0.019 Pulmonary andActivation- 109 (84.2, 150.8) 97 (71, 136) 0.033 Regulated Chemokine(PARC) (ng/mL) Pulmonary surfactant- 7 (4.1, 9.9) 5 (3.1, 8.1) <0.001associated protein D (SP-D) (ng/mL) Resistin (ng/mL) 2.9 (2.1, 4.575)2.3 (1.8, 3.3) <0.001 Serotransferrin (Transferrin) 253.5 (210, 302)275.5 (239.2, 317) 0.003 (mg/dl) Serum Amyloid P-Component 11.5 (9.1,14.8) 13 (10, 16) 0.015 (SAP) (ug/mL) Stem Cell Factor (SCF) (pg/mL) 432(314.8, 621) 348.5 (274, 443.2) <0.001 T-Cell-Specific Protein RANTES8.1 (3.5, 18.8) 8.5 (3.9, 17) 0.866 (RANTES) (ng/mL) Tamm-HorsfallUrinary 0.022 (0.013, 0.03) 0.032 (0.022, 0.041) <0.001 Glycoprotein(THP) (ug/mL) Thrombomodulin (TM) 4.45 (3.6, 6.3) 3.6 (3, 4.4) <0.001(ng/mL) Thrombospondin-1 (ng/mL) 3670 (2010, 7210) 4305 (2170, 7392)0.404 Thyroid-Stimulating Hormone 1.3 (0.718, 1.9) 1.2 (0.802, 1.7)0.709 (TSH) (uIU/mL) Thyroxine-Binding Globulin 36 (31, 41.8) 38 (31,45) 0.177 (TBG) (ug/mL) Tissue Inhibitor of 94.5 (77, 118.8) 70 (58, 87)<0.001 Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin (TTR) (mg/dl)23 (19, 28.8) 26 (22, 30) 0.001 Troponin (pg/ml) 39 (15.2, 184.3) 6.5(3.3, 18.1) <0.001 Tumor necrosis factor 9.4 (6.3, 15) 6 (4.7, 8.1)<0.001 receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 705.5 (544,982.2) 545 (448.2, 681.8) <0.001 Molecule-1 (VCAM-1) (ng/mL) VascularEndothelial Growth 103.5 (69.5, 162.5) 101 (72, 135) 0.415 Factor (VEGF)(pg/mL) Vitamin D-Binding Protein 233.5 (174.2, 306.8) 250.5 (195.2,309.2) 0.189 (VDBP) (ug/mL) Vitamin K-Dependent Protein 13 (11, 16) 14(11, 16) 0.454 S (VKDPS) (ug/mL) Vitronectin (ug/mL) 426 (328.2, 534.5)463.5 (358, 591) 0.043 von Willebrand Factor (vWF) 182.5 (134.2, 233.5)123 (91, 171) <0.001 (ug/mL)

Table 2B below shows biomarker concentrations and their prognosticassociation that differ between those in the training set (N=648) with amajor adverse cardiac event (MACE) from 3-365 days of the blood draw andthose who did not. The numbers in this table were calculated using thecomposite endpoint of one-year MACE with CV death, MI, or major stroke;these proteins produce similar results with the composite endpoint ofone-year MACE with all-cause death, MI and/or major stroke.

TABLE 2B Prognostic Biomarkers (3-365 Days Post-Cath) (Training Set)Concentration in Concentration in Subjects with Subjects withoutone-year MACE one-year MACE Biomarker (N = 71) (N = 577) p-valueAdiponectin (ug/mL) 5.1 (3.3, 7.95) 3.6 (2.3, 5.6) <0.001Alpha-1-Antitrypsin (AAT) 2 (1.7, 2.4) 1.8 (1.5, 2.1) <0.001 (mg/mL)Alpha-2-Macroglobulin 2.1 (1.75, 2.5) 1.9 (1.6, 2.3) 0.002 (A2Macro)(mg/mL) Angiopoietin-1 (ANG-1) 6.8 (4.9, 10) 6.8 (5, 9.8) 0.873 (ng/mL)Angiotensin-Converting 74 (59, 105) 79 (60, 105) 0.38 Enzyme (ACE)(ng/mL) Apolipoprotein(a) (Lp(a)) 199 (109, 420) 166.5 (59, 455.5) 0.271(ug/mL) Apolipoprotein A-I (Apo A-I) 1.8 (1.4, 2) 1.8 (1.5, 2.1) 0.279(mg/mL) Apolipoprotein A-II (Apo A-II) 263 (215, 331.5) 312 (255, 380)<0.001 (ng/mL) Apolipoprotein B (Apo B) 1280 (952.5, 1665) 1390 (1090,1820) 0.063 (ug/mL) Apolipoprotein C-I (Apo C-I) 308 (250.5, 356.5) 314(262, 380.2) 0.166 (ng/mL) Apolipoprotein C-III (Apo C-III) 222 (168.5,272) 216 (158.8, 271) 0.471 (ug/mL) Apolipoprotein H (Apo H) 337 (278.5,397.5) 332 (270, 387.2) 0.54 (ug/mL) Beta-2-Microglobulin (B2M) 2.5(1.8, 3.6) 1.7 (1.3, 2.2) <0.001 (ug/mL) Brain-Derived Neurotrophic 1.9(0.745, 4.05) 2.35 (1, 4.6) 0.192 Factor (BDNF) (ng/mL) C-ReactiveProtein (CRP) 7 (2.95, 19.5) 3.3 (1.4, 8.325) <0.001 (ug/mL) Carbonicanhydrase 9 (CA-9) 0.23 (0.16, 0.36) 0.14 (0.085, 0.232) <0.001 (ng/mL)Carcinoembryonic antigen- 24 (21, 29) 23 (20, 28) 0.28 related celladhesion molecule 1 (CEACAM1) (ng/mL) CD5 Antigen-like (CD5L) 4090(2770, 6630) 3700 (2798, 5010) 0.071 (ng/mL) Decorin (ng/mL) 2.9 (2.15,4.25) 2.3 (1.9, 3.1) <0.001 E-Selectin (ng/mL) 5.1 (3.5, 7) 5.1 (3.7, 7)0.536 EN-RAGE (ng/mL) 35 (16, 58.5) 26 (16, 49) 0.219 Eotaxin-1 (pg/mL)118 (42.5, 152) 97 (42.5, 144.2) 0.068 Factor VII (ng/mL) 395 (293,539.5) 468 (364, 587) 0.009 Ferritin (FRTN) (ng/mL) 133 (84, 253) 133(68, 217.2) 0.141 Fetuin-A (ug/mL) 651 (541.5, 766.5) 692.5 (588.8,827.2) 0.021 Fibrinogen (mg/mL) 4.9 (4.1, 5.75) 4.2 (3.5, 5.3) <0.001Follicle-Stimulating Hormone 8 (4.7, 35) 6.3 (3.6, 26.2) 0.151 (FSH)(mIU/mL) Growth Hormone (GH) 0.56 (0.195, 0.98) 0.28 (0.07, 0.802) 0.009(ng/mL) Haptoglobin (mg/mL) 1.2 (0.67, 2.2) 1.15 (0.54, 1.9) 0.234Immunoglobulin A (IgA) 2.2 (1.6, 3.3) 2.4 (1.5, 3.4) 0.839 (mg/mL)Immunoglobulin M (IgM) 1.4 (1.1, 1.9) 1.4 (0.94, 2.2) 0.969 (mg/mL)Insulin (uIU/mL) 1 (0.11, 1.9) 0.815 (0.11, 2.1) 0.806 IntercellularAdhesion 106 (83.5, 130.5) 104 (84, 132.2) 0.81 Molecule 1 (ICAM-1)(ng/mL) Interferon gamma Induced 336 (246, 437) 300 (228, 398) 0.076Protein 10 (IP-10) (pg/mL) Interleukin-1 receptor 121 (95.5, 158) 114.5(88, 148) 0.224 antagonist (IL-1ra) (pg/mL) Interleukin-6 receptor(IL-6r) 23 (19, 30) 24 (19, 29) 0.782 (ng/mL) Interleukin-8 (IL-8)(pg/mL) 11 (6.7, 16.5) 6 (4.2, 9.1) <0.001 Interleukin-12 Subunit p40(IL- 0.64 (0.48, 0.765) 0.58 (0.458, 0.71) 0.193 12p40) (ng/mL)Interleukin-15 (IL-15) (ng/mL) 0.55 (0.46, 0.69) 0.56 (0.45, 0.7) 0.888Interleukin-18 (IL-18) (pg/mL) 203 (160, 295.5) 197 (143.5, 266) 0.188Interleukin-18-binding protein 14 (9.3, 19) 9 (7, 12) <0.001 (IL-18bp)(ng/mL) Interleukin-23 (IL-23) (ng/mL) 2.6 (1.8, 3.2) 2.5 (2, 3.2) 0.604Kidney Injury Molecule-1 0.066 (0.043, 0.14) 0.034 (0.014, 0.059) <0.001(KIM-1) (ng/mL) Leptin (ng/mL) 8.4 (4.6, 19.5) 8.9 (4.2, 21) 0.743Luteinizing Hormone (LH) 5.8 (3.65, 12) 4.75 (3.3, 8.725) 0.061 (mIU/mL)Macrophage Colony- 0.79 (0.51, 1.55) 0.39 (0.16, 0.6) <0.001 StimulatingFactor 1 (M-CSF) (ng/mL) Macrophage Inflammatory 271 (198.5, 404.5) 257(187.5, 345) 0.263 Protein-1 beta (MIP-1 beta) (pg/mL) MatrixMetalloproteinase-2 1500 (1320, 1900) 1320 (1120, 1600) <0.001 (MMP-2)(ng/mL) Matrix Metalloproteinase-3 9.5 (7.1, 17) 6.6 (4.7, 9.8) <0.001(MMP-3) (ng/mL) Matrix Metalloproteinase-7 0.47 (0.315, 0.805) 0.34(0.24, 0.51) <0.001 (MMP-7) (ng/mL) Matrix Metalloproteinase-9 122 (74,194) 127 (91, 178) 0.569 (MMP-9) (ng/mL) Matrix Metalloproteinase-9, 572(366, 951) 579.5 (418.5, 791) 0.977 total (MMP-9, total) (ng/mL) Midkine(ng/mL) 22 (14.5, 37) 13 (10, 19) <0.001 Monocyte Chemotactic 113 (73,161.5) 108 (78, 158) 0.938 Protein 1 (MCP-1) (pg/mL) MonocyteChemotactic 26 (19, 30) 23 (17, 30) 0.135 Protein 2 (MCP-2) (pg/mL)Monocyte Chemotactic 2370 (1635, 3320) 2295 (1668, 3362) 0.892 Protein 4(MCP-4) (pg/mL) Monokine Induced by Gamma 1590 (915.5, 2690) 879 (557.5,1602) <0.001 Interferon (MIG) (pg/mL) Myeloid Progenitor Inhibitory 1.5(1.1, 2) 1.2 (0.93, 1.5) <0.001 Factor 1 (MPIF-1) (ng/mL) Myoglobin(ng/mL) 49 (30, 86) 30 (21, 45.2) <0.001 N-terminal prohormone of 6470(2220, 15980) 1330 (461.5, 3302) <0.001 brain natriuretic peptide (NTproBNP) (pg/mL) Osteopontin (ng/mL) 53 (29.5, 83.5) 26 (19, 37) <0.001Pancreatic Polypeptide (PPP) 149 (68, 328) 87 (46.5, 150.5) <0.001(pg/mL) Plasminogen Activator 46 (26, 66.5) 44 (26, 73) 0.766 Inhibitor1 (PAI-1) (ng/mL) Platelet endothelial cell 56 (46, 70) 54 (45, 63)0.191 adhesion molecule (PECAM-1) (ng/mL) Prolactin (PRL) (ng/mL) 9.7(6.3, 15) 8 (5.5, 12) 0.029 Pulmonary and Activation- 108 (86, 141.5) 97(71, 137) 0.056 Regulated Chemokine (PARC) (ng/mL) Pulmonary surfactant-7.2 (4.1, 9.8) 5.1 (3.1, 8.1) 0.002 associated protein D (SP-D) (ng/mL)Resistin (ng/mL) 3.1 (2.1, 4.65) 2.3 (1.8, 3.3) <0.001 Serotransferrin(Transferrin) 253 (206.5, 303.5) 274.5 (239, 317) 0.007 (mg/dl) SerumAmyloid P-Component 11 (9.2, 14.5) 13 (10, 16) 0.014 (SAP) (ug/mL) StemCell Factor (SCF) (pg/mL) 449 (317.5, 635) 349 (274, 444) <0.001T-Cell-Specific Protein RANTES 8.2 (3.7, 17) 8.4 (3.9, 17) 0.801(RANTES) (ng/mL) Tamm-Horsfall Urinary 0.022 (0.013, 0.03) 0.032 (0.022,0.041) <0.001 Glycoprotein (THP) (ug/mL) Thrombomodulin (TM) 4.7 (3.7,6.95) 3.6 (3, 4.4) <0.001 (ng/mL) Thrombospondin-1 (ng/mL) 3660 (1860,6960) 4305 (2170, 7445) 0.359 Thyroid-Stimulating Hormone 1.2 (0.625,1.85) 1.2 (0.808, 1.8) 0.686 (TSH) (uIU/mL) Thyroxine-Binding Globulin36 (32, 41.5) 37.5 (31, 45) 0.46 (TBG) (ug/mL) Tissue Inhibitor of 96(78, 126.5) 71 (58, 87) <0.001 Metalloproteinases 1 (TIMP-1) (ng/mL)Transthyretin (TTR) (mg/dl) 23 (18, 28.5) 26 (22, 30) <0.001 Troponin(pg/ml) 38.5 (15.2, 185.7) 6.6 (3.3, 18.3) <0.001 Tumor necrosis factor10 (6.7, 15) 6 (4.7, 8.1) <0.001 receptor 2 (TNFR2) (ng/mL) VascularCell Adhesion 792 (582, 1015) 543.5 (448.8, 680.2) <0.001 Molecule-1(VCAM-1) (ng/mL) Vascular Endothelial Growth 104 (67.5, 166) 100 (72,135) 0.243 Factor (VEGF) (pg/mL) Vitamin D-Binding Protein 231 (176.5,309.5) 250 (195.8, 307.8) 0.316 (VDBP) (ug/mL) Vitamin K-DependentProtein 13 (11, 16) 14 (11, 16) 0.493 S (VKDPS) (ug/mL) Vitronectin(ug/mL) 424 (326.5, 542.5) 463.5 (355.8, 591) 0.062 von WillebrandFactor (vWF) 190 (140.5, 238.5) 123 (91, 171) <0.001 (ug/mL)

Table 3A below shows baseline clinical variables and their diagnosticassociation that differ between those in the training set (N=649) withat least one coronary artery stenosis ≥70% (N=428) and those who did notin the cohort of subjects who received a coronary cath, with or withoutan optional peripheral cath.

TABLE 3A Diagnostic Clinical Variables (Received Coronary Cath;Peripheral Cath Optional) (Training Set) Subjects with Subjects w/oCoronary Stenosis ≥70% Coronary Stenosis ≥70% Clinical Characteristics(N = 428) (N = 221) p-value Demographics Age (years) 67.3 (11.5) 64(11.7) <0.001 Male sex 337/428 (78.7%) 129/221 (58.4%) <0.001 Caucasian408/428 (95.3%) 204/221 (92.3%) 0.152 Vital Signs Heart rate (beat/min)69 (12.9) 70.4 (14.5) 0.246 Systolic BP (mmHg) 138.1 (23.8) 135.1 (20.5)0.101 Diastolic BP (mmHg) 72.6 (11.7) 73 (10.9) 0.658 Medical HistorySmoking 60/424 (14.2%) 32/219 (14.6%) 0.906 Atrial fibrillation/flutter79/428 (18.5%) 49/221 (22.2%) 0.298 Hypertension 338/428 (79%) 147/221(66.5%) <0.001 Coronary artery disease 288/428 (67.3%) 58/221 (26.2%)<0.001 Myocardial infarction 138/428 (32.2%) 26/221 (11.8%) <0.001 Heartfailure 94/428 (22%) 54/221 (24.4%) 0.491 Peripheral artery disease102/428 (23.8%) 29/221 (13.1%) 0.001 COPD 74/428 (17.3%) 45/221 (20.4%)0.338 Diabetes, Type 1 9/428 (2.1%) 3/221 (1.4%) 0.76 Diabetes, Type 2125/428 (29.2%) 30/221 (13.6%) <0.001 Any Diabetes 134/428 (31.3%)33/221 (14.9%) <0.001 CVA/TIA 46/428 (10.7%) 22/221 (10%) 0.789 Chronickidney disease 68/428 (15.9%) 10/221 (4.5%) <0.001 Hemodialysis 12/426(2.8%) 3/221 (1.4%) 0.285 Angioplasty, peripheral 64/428 (15%) 12/221(5.4%) <0.001 and/or coronary Stent, peripheral and/or 153/428 (35.7%)36/221 (16.3%) <0.001 coronary CABG 126/428 (29.4%) 4/221 (1.8%) <0.001Percutaneous coronary 183/428 (42.8%) 6/221 (2.7%) <0.001 interventionMedications ACE-I/ARB 249/427 (58.3%) 102/220 (46.4%) 0.005 Beta blocker342/427 (80.1%) 133/220 (60.5%) <0.001 Aldosterone antagonist 17/427(4%) 8/220 (3.6%) 1 Loop diuretics 94/427 (22%) 52/220 (23.6%) 0.691Nitrates 110/427 (25.8%) 18/220 (8.2%) <0.001 CCB 105/427 (24.6%) 44/220(20%) 0.201 Statin 337/426 (79.1%) 129/220 (58.6%) <0.001 Aspirin349/425 (82.1%) 136/220 (61.8%) <0.001 Warfarin 58/427 (13.6%) 44/220(20%) 0.04 Clopidogrel 114/426 (26.8%) 30/220 (13.6%) <0.001Echocardiographic results LVEF (%) 55.4 (15.3) 57.3 (15.6) 0.245 RSVP(mmHg) 40.9 (10.8) 41.3 (11.8) 0.817 Stress test results Ischemia onScan 105/131 (80.2%) 22/43 (51.2%) <0.001 Ischemia on ECG 57/113 (50.4%)14/41 (34.1%) 0.099 Angiography results >=70% coronary stenosis 274/428(64%) 0/221 (0%) <0.001 in >=2 vessels >=70% coronary stenosis 152/428(35.5%) 0/221 (0%) <0.001 in >=3 vessels Lab Measures Sodium 139.2 ± 3140.1 ± 3.3 0.004 Blood urea nitrogen 18 (15, 25) 17 (14, 22) 0.002(mg/dL) Creatinine (mg/dL) 1.1 (0.9, 1.4) 1 (0.9, 1.2) <0.001 eGFR(median, CKDEPI) 96.9 (69, 110.1) 103.8 (84.6, 112.6) <0.001 Totalcholesterol (mg/dL) 141.4 (39.1) 160.5 (49) <0.001 LDL cholesterol(mg/dL) 75.4 (31.1) 90.3 (37.8) <0.001 Glycohemoglobin (%) 6.4 (5.6,7.1) 5.7 (5.4, 6) <0.001 Glucose (mg/dL) 104 (93, 129.5) 99 (89, 108.5)0.001 HGB (mg/dL) 13.1 (1.8) 13.4 (1.7) 0.024 All continuous variablesare displayed as mean ± standard deviation, unless otherwise specified.BP = blood pressure, MI = myocardial infarction, COPD = chronicobstructive pulmonary disease, CVA/TIA = cerebrovascularaccident/transient ischemic attack, CKD = chronic kidney disease, CABG =coronary artery by-pass graft, ACE-I/ARB = angiotensin converting enzymeinhibitor/angiotensin receptor blocker, CCB = calcium channel blocker,LVEF = left ventricular ejection fraction, RVSP = right ventricularsystolic pressure, eGFR = estimated glomerular filtration rate, LDL =low density lipoprotein, HGB = hemoglobin.

Table 3B below shows baseline clinical variables and their diagnosticassociation that differ between those in the training set (N=566) withat least one coronary artery stenosis ≥70% (N=361) and those who did notin the cohort of subjects who received a coronary cath only.

TABLE 3B Diagnostic Clinical Variables (Received Coronary Cath Only)(Training Set) Subjects with Subjects w/o Coronary Stenosis ≥70%Coronary Stenosis ≥70% Clinical Characteristics (N = 361) (N = 205)p-value Demographics Age (years) 67 (11.7) 63.8 (11.7) 0.002 Male sex283/361 (78.4%) 118/205 (57.6%) <0.001 Caucasian 341/361 (94.5%) 191/205(93.2%) 0.582 Vital Signs Heart rate (beat/min) 69.3 (13.2) 70.7 (14.7)0.268 Systolic BP (mmHg) 136.4 (22.9) 134.5 (19.9) 0.311 Diastolic BP(mmHg) 72.6 (11.9) 73.1 (11) 0.604 Medical History Smoking 49/358(13.7%) 27/203 (13.3%) 1 Atrial fibrillation/flutter 70/361 (19.4%)48/205 (23.4%) 0.282 Hypertension 282/361 (78.1%) 133/205 (64.9%) <0.001Coronary artery disease 235/361 (65.1%) 46/205 (22.4%) <0.001 Myocardialinfarction 116/361 (32.1%) 16/205 (7.8%) <0.001 Heart failure 78/361(21.6%) 50/205 (24.4%) 0.465 Peripheral artery disease 72/361 (19.9%)21/205 (10.2%) 0.003 COPD 61/361 (16.9%) 43/205 (21%) 0.259 Diabetes,Type 1 7/361 (1.9%) 3/205 (1.5%) 1 Diabetes, Type 2 97/361 (26.9%)28/205 (13.7%) <0.001 Any Diabetes 104/361 (28.8%) 31/205 (15.1%) <0.001CVA/TIA 32/361 (8.9%) 20/205 (9.8%) 0.763 Chronic kidney disease 52/361(14.4%) 9/205 (4.4%) <0.001 Hemodialysis 9/359 (2.5%) 3/205 (1.5%) 0.55Angioplasty, peripheral 52/361 (14.4%) 7/205 (3.4%) <0.001 and/orcoronary Stent, peripheral and/or 124/361 (34.3%) 26/205 (12.7%) <0.001coronary CABG 94/361 (26%) 1/205 (0.5%) <0.001 Percutaneous coronary166/361 (46%) 6/205 (2.9%) <0.001 intervention Medications ACE-I/ARB209/361 (57.9%) 92/204 (45.1%) 0.004 Beta blocker 289/361 (80.1%)120/204 (58.8%) <0.001 Aldosterone antagonist 12/361 (3.3%) 8/204 (3.9%)0.813 Loop diuretics 76/361 (21.1%) 48/204 (23.5%) 0.526 Nitrates 89/361(24.7%) 14/204 (6.9%) <0.001 CCB 86/361 (23.8%) 40/204 (19.6%) 0.293Statin 280/360 (77.8%) 117/204 (57.4%) <0.001 Aspirin 292/359 (81.3%)124/204 (60.8%) <0.001 Warfarin 51/361 (14.1%) 43/204 (21.1%) 0.035Clopidogrel 96/361 (26.6%) 23/204 (11.3%) <0.001 Echocardiographicresults LVEF (%) 55.2 (15.3) 57.6 (15.9) 0.18 RSVP (mmHg) 41.2 (11.2)41.5 (11.9) 0.862 Stress test results Ischemia on Scan 89/112 (79.5%)20/37 (54.1%) 0.005 Ischemia on ECG 48/97 (49.5%) 14/36 (38.9%) 0.33Angiography results >=70% coronary stenosis 220/361 (60.9%) 0/205 (0%)<0.001 in >=2 vessels >=70% coronary stenosis 110/361 (30.5%) 0/205 (0%)<0.001 in >=3 vessels Lab Measures Sodium 139.2 (3.1) 140.1 (3.3) 0.005Blood urea nitrogen 18 (15, 25) 17 (14, 22.2) 0.012 (mg/dL) Creatinine(mg/dL) 1.1 (0.9, 1.4) 1 (0.9, 1.2) <0.001 eGFR (median, CKDEPI) 99.4(69.8, 111.1) 103.8 (84.6, 112.5) 0.012 Total cholesterol (mg/dL) 140.7(37.1) 164.6 (48.4) <0.001 LDL cholesterol (mg/dL) 75.2 (29.4) 93.3(37.9) <0.001 Glycohemoglobin (%) 6.3 (5.6, 7.1) 5.7 (5.4, 6) 0.001Glucose (mg/dL) 103 (93, 125.2) 99 (89, 107.8) 0.006 HGB (mg/dL) 13.1(1.7) 13.5 (1.7) 0.02 All continuous variables are displayed as mean ±standard deviation, unless otherwise specified. BP = blood pressure, MI= myocardial infarction, COPD = chronic obstructive pulmonary disease,CVA/TIA = cerebrovascular accident/transient ischemic attack, CKD =chronic kidney disease, CABG = coronary artery by-pass graft, ACE-I/ARB= angiotensin converting enzyme inhibitor/angiotensin receptor blocker,CCB = calcium channel blocker, LVEF = left ventricular ejectionfraction, RVSP = right ventricular systolic pressure, eGFR = estimatedglomerular filtration rate, LDL = low density lipoprotein, HGB =hemoglobin.

Table 4A below shows baseline clinical variables and their prognosticassociation that differ between those in the training set (N=649) inpatients with major adverse cardiac events (MACE) within 365 days of theblood draw. The numbers in this table were calculated using thecomposite endpoint of one-year MACE with CV death, MI, or major stroke;these proteins produce similar results with the composite endpoint ofone-year MACE with all-cause death, MI and/or major stroke.

TABLE 4A Prognostic Clinical Variables (Within 365 Days Post-Cath)(Training Set) Concentration in Concentration in Subjects with Subjectswithout one-year MACE one-year MACE Clinical Characteristics (N = 82) (N= 567) p-value Demographics Age (years) 71.9 (11.7) 65.3 (11.4) <0.001Male sex 60/82 (73.2%) 406/567 (71.6%) 0.896 Caucasian 78/82 (95.1%)534/567 (94.2%) 1 Vital Signs Heart rate (beat/min) 72.1 (14) 69.1(13.4) 0.071 Systolic BP (mmHg) 133.4 (25.9) 137.6 (22.2) 0.173Diastolic BP (mmHg) 69.9 (11.5) 73.2 (11.4) 0.02 Medical History Smoking13/82 (15.9%) 79/561 (14.1%) 0.617 Atrial fibrillation/flutter 16/82(19.5%) 112/567 (19.8%) 1 Hypertension 69/82 (84.1%) 416/567 (73.4%)0.041 Coronary artery disease 51/82 (62.2%) 295/567 (52%) 0.097 Prior MI28/82 (34.1%) 136/567 (24%) 0.057 Heart failure 35/82 (42.7%) 113/567(19.9%) <0.001 Peripheral artery disease 22/82 (26.8%) 109/567 (19.2%)0.14 COPD 26/82 (31.7%) 93/567 (16.4%) 0.002 Diabetes, Type 1 2/82(2.4%) 10/567 (1.8%) 0.655 Diabetes, Type 2 38/82 (46.3%) 117/567(20.6%) <0.001 Any Diabetes 40/82 (48.8%) 127/567 (22.4%) <0.001 CVA/TIA11/82 (13.4%) 57/567 (10.1%) 0.338 Chronic kidney disease 26/82 (31.7%)52/567 (9.2%) <0.001 Hemodialysis 6/81 (7.4%) 9/566 (1.6%) 0.006Angioplasty, peripheral 8/82 (9.8%) 68/567 (12%) 0.713 and/or coronaryStent, peripheral and/or 31/82 (37.8%) 158/567 (27.9%) 0.069 coronaryCABG 21/82 (25.6%) 109/567 (19.2%) 0.185 Percutaneous coronary 26/82(31.7%) 163/567 (28.7%) 0.604 intervention Medications ACE-I/ARB 50/82(61%) 301/565 (53.3%) 0.195 Beta blocker 60/82 (73.2%) 415/565 (73.5%) 1Aldosterone antagonist 3/82 (3.7%) 22/565 (3.9%) 1 Loop diuretics 32/82(39%) 114/565 (20.2%) <0.001 Nitrates 23/82 (28%) 105/565 (18.6%) 0.053CCB 21/82 (25.6%) 128/565 (22.7%) 0.575 Statin 61/82 (74.4%) 405/564(71.8%) 0.694 Aspirin 61/82 (74.4%) 424/563 (75.3%) 0.891 Warfarin 12/82(14.6%) 90/565 (15.9%) 0.872 Clopidogrel 24/82 (29.3%) 120/564 (21.3%)0.118 Echocardiographic results LVEF (%) 49.3 (16.8) 57.3 (14.8) <0.001RSVP (mmHg) 44.2 (11.3) 40.4 (11.1) 0.067 Stress test results Ischemiaon Scan 17/20 (85%) 110/154 (71.4%) 0.286 Ischemia on ECG 6/15 (40%)65/139 (46.8%) 0.787 Angiography results >=70% coronary stenosis 52/82(63.4%) 222/567 (39.2%) <0.001 in >=2 vessels >=70% coronary stenosis27/82 (32.9%) 125/567 (22%) 0.036 in >=3 vessels Lab Measures Sodium138.6 (3.5) 139.6 (3.1) 0.026 Blood urea nitrogen 25 (18.5, 37) 17 (14,22.5) <0.001 (mg/dL) Creatinine (mg/dL) 1.3 (1.1, 1.9) 1.1 (0.9, 1.3)<0.001 eGFR (median, CKDEPI) 67.7 (45.3, 89.3) 102.3 (79.7, 112.3)<0.001 Total cholesterol (mg/dL) 140.5 (51.3) 147.2 (40.8) 0.333 LDLcholesterol (mg/dL) 77.5 (42.9) 79.4 (31.6) 0.735 Glycohemoglobin (%)6.5 (5.7, 7.7) 6 (5.5, 6.9) 0.077 Glucose (mg/dL) 114 (101, 145) 100(90, 120) <0.001 HGB (mg/dL) 12.2 (1.9) 13.3 (1.7) <0.001 All continuousvariables are displayed as mean ± standard deviation, unless otherwisespecified. BP = blood pressure, MI = myocardial infarction, COPD =chronic obstructive pulmonary disease, CVA/TIA = cerebrovascularaccident/transient ischemic attack, CKD = chronic kidney disease, CABG =coronary artery by-pass graft, ACE-I/ARB = angiotensin converting enzymeinhibitor/angiotensin receptor blocker, CCB = calcium channel blocker,LVEF = left ventricular ejection fraction, RVSP = right ventricularsystolic pressure, eGFR = estimated glomerular filtration rate, LDL =low density lipoprotein, HGB = hemoglobin.

Table 4B below shows baseline clinical variables and their prognosticassociation that differ between those in the training set (N=648) with amajor adverse cardiac event (MACE) from 3-365 days of the blood draw andthose who did not. The numbers in this table were calculated using thecomposite endpoint of one-year MACE with CV death, MI, or major stroke;these proteins produce similar results with the composite endpoint ofone-year MACE with all-cause death, MI and/or major stroke.

TABLE 4B Prognostic Clinical Variables (3-365 Days Post-Cath) (TrainingSet) Concentration in Concentration in Subjects with Subjects withoutone-year MACE one-year MACE Clinical Characteristics (N = 71) (N = 577)p-value Demographics Age (years) 73 (11) 65.3 (11.5) <0.001 Male sex50/71 (70.4%) 415/577 (71.9%) 0.781 Caucasian 67/71 (94.4%) 544/577(94.3%) 1 Vital Signs Heart rate (beat/min) 72.4 (14.1) 69.1 (13.4)0.068 Systolic BP (mmHg) 132.9 (26.5) 137.5 (22.2) 0.163 Diastolic BP(mmHg) 69.3 (11.8) 73.2 (11.3) 0.012 Medical History Smoking 10/71(14.1%) 82/571 (14.4%) 1 Atrial fibrillation/flutter 15/71 (21.1%)113/577 (19.6%) 0.753 Hypertension 62/71 (87.3%) 423/577 (73.3%) 0.009Coronary artery disease 46/71 (64.8%) 300/577 (52%) 0.044 Prior MI 26/71(36.6%) 138/577 (23.9%) 0.029 Heart failure 29/71 (40.8%) 119/577(20.6%) <0.001 Peripheral artery disease 20/71 (28.2%) 111/577 (19.2%)0.085 COPD 23/71 (32.4%) 95/577 (16.5%) 0.003 Diabetes, Type 1 2/71(2.8%) 10/577 (1.7%) 0.631 Diabetes, Type 2 33/71 (46.5%) 122/577(21.1%) <0.001 Any Diabetes 35/71 (49.3%) 132/577 (22.9%) <0.001 CVA/TIA10/71 (14.1%) 58/577 (10.1%) 0.304 Chronic kidney disease 23/71 (32.4%)55/577 (9.5%) <0.001 Hemodialysis 6/71 (8.5%) 9/575 (1.6%) 0.003Angioplasty, peripheral 7/71 (9.9%) 69/577 (12%) 0.7 and/or coronaryStent, peripheral and/or 28/71 (39.4%) 161/577 (27.9%) 0.052 coronaryCABG 20/71 (28.2%) 110/577 (19.1%) 0.083 Percutaneous coronary 20/71(28.2%) 168/577 (29.1%) 1 intervention Medications ACE-I/ARB 41/71(57.7%) 310/575 (53.9%) 0.614 Beta blocker 54/71 (76.1%) 421/575 (73.2%)0.671 Aldosterone antagonist 2/71 (2.8%) 23/575 (4%) 1 Loop diuretics30/71 (42.3%) 116/575 (20.2%) <0.001 Nitrates 22/71 (31%) 106/575(18.4%) 0.017 CCB 19/71 (26.8%) 130/575 (22.6%) 0.456 Statin 53/71(74.6%) 412/574 (71.8%) 0.675 Aspirin 51/71 (71.8%) 433/573 (75.6%)0.471 Warfarin 12/71 (16.9%) 90/575 (15.7%) 0.733 Clopidogrel 21/71(29.6%) 122/574 (21.3%) 0.129 Echocardiographic results LVEF (%) 50.3(16.9) 57 (15) 0.008 RSVP (mmHg) 44.2 (11.6) 40.5 (11) 0.098 Stress testresults Ischemia on Scan 14/16 (87.5%) 113/158 (71.5%) 0.241 Ischemia onECG 4/12 (33.3%) 67/142 (47.2%) 0.387 Angiography results >=70% coronarystenosis 44/71 (62%) 229/577 (39.7%) <0.001 in >=2 vessels >=70%coronary stenosis 25/71 (35.2%) 127/577 (22%) 0.017 in >=3 vessels LabMeasures Sodium 138.7 (3.6) 139.6 (3.1) 0.058 Blood urea nitrogen 25.5(18.8, 36.5) 17 (14, 23) <0.001 (mg/dL) Creatinine (mg/dL) 1.3 (1.1,1.9) 1.1 (0.9, 1.3) <0.001 eGFR (median, CKDEPI) 65.7 (42.9, 86) 102.2(79.1, 112.4) <0.001 Total cholesterol (mg/dL) 135.6 (44.2) 147.8 (42.2)0.062 LDL cholesterol (mg/dL) 73.6 (36.2) 80 (33.1) 0.221Glycohemoglobin (%) 6.5 (5.7, 7.2) 6 (5.5, 6.9) 0.117 Glucose (mg/dL)114 (101, 144) 101 (91, 120) <0.001 HGB (mg/dL) 12 (1.8) 13.4 (1.7)<0.001 All continuous variables are displayed as mean ± standarddeviation, unless otherwise specified. BP = blood pressure, MI =myocardial infarction, COPD = chronic obstructive pulmonary disease,CVA/TIA = cerebrovascular accident/transient ischemic attack, CKD =chronic kidney disease, CABG = coronary artery by-pass graft, ACE-I/ARB= angiotensin converting enzyme inhibitor/angiotensin receptor blocker,CCB = calcium channel blocker, LVEF = left ventricular ejectionfraction, RVSP = right ventricular systolic pressure, eGFR = estimatedglomerular filtration rate, LDL = low density lipoprotein, HGB =hemoglobin.

Diagnostic and Prognostic Methods

The methods of the invention relate generally to providing a diagnosisand/or prognosis of a cardiovascular disease or outcome in a subject,comprising the steps of: (i) determining the level of at least onebiomarker in a biological sample obtained from the subject, particularlywhere the biomarkers are selected from the group consisting of those setforth in Tables 1A, 1B, 2A and 2B; (ii) optionally, determining thestatus of at least one clinical variable for the subject, where theclinical variable is selected from the group consisting of those setforth in Tables 3A, 3B, 4A and 4B; (iii) calculating a diagnostic orprognostic score based on the levels of the biomarkers determined instep (i) and, optionally, the status of the clinical variable(s)determined in step (ii); (iv) classifying the diagnostic or prognosticscore as a positive or negative result; and (v) determining atherapeutic or diagnostic intervention regimen based on the positive ornegative result.

Embodiments of the present invention provide methods for evaluatingcardiovascular status in a subject, comprising: (i) obtaining a samplefrom a subject selected for evaluation; (ii) performing one or moreassays configured to detect a biomarker selected from the groupconsisting of those set forth in Tables 1A, 1B, 2A, and 2B byintroducing the sample obtained from the subject into an assayinstrument which (a) contacts the sample with one or more antibodieswhich specifically bind for the detection of the biomarker(s) which areassayed, and (b) generates one or more assay results indicative ofbinding of each biomarker which is assayed to a respective antibody toprovide one or more assay results; (iii) optionally, determining thestatus of at least one clinical variable for the subject, wherein theclinical variable is selected from the group consisting of those setforth in Tables 3A, 3B, 4A, and 4B; (iv) correlating the assay result(s)generated by the assay instrument and optionally the clinical variablestatus to the cardiovascular status of the subject, wherein saidcorrelation step comprises correlating the assay result(s) to one ormore of risk stratification, prognosis, diagnosis, classifying andmonitoring of the cardiovascular status of the subject, wherein saidcorrelating step comprises assigning a likelihood of a positive ornegative diagnosis, or one or more future changes in cardiovascularstatus to the subject based on the assay result(s); and (v) treating thepatient based on the predetermined subpopulation of individuals to whichthe patient is assigned, wherein the treatment comprises a therapeuticor diagnostic intervention regimen.

Embodiments of the present invention provide a method for diagnosingobstructive coronary artery disease in a subject comprising: (i)obtaining a sample from a subject selected for evaluation; (ii)performing one or more assays configured to detect a biomarker selectedfrom the group consisting of those set forth in Tables 1A and 1B byintroducing the sample obtained from the subject into an assayinstrument which (a) contacts the sample with one or more antibodieswhich specifically bind for the detection of the biomarker(s) which areassayed, and (b) generates one or more assay results indicative ofbinding of each biomarker which is assayed to a respective antibody toprovide one or more assay results; (iii) optionally, determining thestatus of at least one clinical variable for the subject, wherein theclinical variable is selected from the group consisting of those setforth in Tables 3A and 3B; (iv) correlating the assay result(s)generated by the assay instrument and optionally the clinical variablestatus to obstructive coronary artery disease, wherein said correlationstep comprises correlating the assay result(s) and optionally theclinical variable(s) to a diagnostic score, wherein said correlatingstep comprises assigning the score to a positive or negative result; and(v) treating the patient based on the positive or negative result,wherein the treatment comprises a therapeutic or diagnostic interventionregimen.

In still other embodiments, the present disclosure provides methods forthe prognosis of a cardiac outcome in a subject within time endpoints,comprising: (i) obtaining a sample from a subject selected forevaluation; (ii) performing one or more assays configured to detect abiomarker selected from the group consisting of those set forth inTables 2A and 2B by introducing the sample obtained from the subjectinto an assay instrument which (a) contacts the sample with one or moreantibodies which specifically bind for detection of the biomarker(s)which are assayed, and (b) generates one or more assay resultsindicative of binding of each biomarker which is assayed to a respectiveantibody to provide one or more assay results; (iii) optionally,determining the status of at least one clinical variable for thesubject, wherein the clinical variable is selected from the groupconsisting of those set forth in Tables 4A and 4B; (iv) correlating theassay result(s) generated by the assay instrument and optionally theclinical variable status to the likelihood of a cardiac outcome in thesubject, wherein said correlation step comprises correlating the assayresult(s) and optionally the clinical variable(s) to a prognostic score,wherein said correlating step comprises assigning the score to apositive or negative result; and (v) treating the patient based on thepositive or negative result, wherein the treatment comprises atherapeutic or diagnostic intervention regimen.

In certain specific embodiments, biomarkers, optionally used inconjunction with clinical variables, can be used in the methods of thepresent invention for the diagnosis of obstructive coronary arterydisease. In other embodiments, biomarkers, optionally used inconjunction with clinical variables, can be used in the prognosis ofcardiovascular outcomes, including but not limited to cardiovasculardeath, myocardial infarct, and stroke. The methods can also be used, forexample, to predict the risk of a composite endpoint, which is acombination of various clinical events that might happen, such ascardiovascular death, myocardial infarct, or stroke where any one ofthose events would count as part of the composite endpoint. Thecomposite may include all-cause death, which is inclusive ofcardiovascular death.

In certain specific embodiments, the biomarkers and/or clinicalvariables used in accordance with the methods of the present inventioninclude those listed in Tables 1A, 1B, 2A, 2B, 3A, 3B, 4A and 4B,particularly those which are associated with a p-value of less than 0.1,less than 0.05, less than 0.01 or less than 0.001.

In some embodiments, at least 1, at least 2, at least 3 or at least 4biomarkers are used in the methods described herein. In otherembodiments, the number of biomarkers employed can vary, and may includeat least 5, 6, 7, 8, 9, 10, or more. In still other embodiments, thenumber of biomarkers can include at least 15, 20, 25 or 50, or more.

In more specific embodiments, the biomarkers used in the diagnosticmethods of the invention are selected from adiponectin, apolipoproteinC-I, decorin, interleukin-8, kidney injury molecule-1, matrixmetalloproteinase 9, midkine, myoglobin, pulmonary surfactant associatedprotein D, stem cell factor, and troponin.

In other specific embodiments, the biomarkers used in the prognosticmethods of the invention are selected from apolipoprotein A-II, kidneyinjury molecule-1, midkine, N terminal prohormone of brain natriureticprotein (NT-proBNP), osteopontin, tissue inhibitor ofmetalloproteinases-1 (TIMP-1), and vascular cell adhesion molecule.

In some embodiments, the diagnostic or prognostic model will result in anumeric or categorical score that relates the patient's level oflikelihood of CAD, e.g. including but not limited to positive predictivevalue (PPV), negative predictive value (NPV), sensitivity (Sn), orspecificity (Sp) or the risk of a cardiovascular event occurring withinthe specified period of time. The number of levels used by thediagnostic model may be as few as two (“positive” vs. “negative”) or asmany as deemed clinically relevant, e.g., a diagnostic model for CAD mayresult a five-level score, where a higher score indicates a higherlikelihood of disease. Specifically, a score of 1 indicates a strongdegree of confidence in a low likelihood of CAD (determined by thetest's NPV), a score of 5 indicates a strong degree of confidence in ahigh likelihood of CAD (determined by the test's PPV), and a score of 3indicates a moderate likelihood for CAD.

Furthermore, in certain embodiments, upon making a positive diagnosisfor obstructive coronary artery disease in the subject according to themethods disclosed herein, a medical practitioner can advantageously usethe diagnosis to identify the need for a therapeutic, diagnostic orother intervention in the subject, particularly an intervention selectedfrom one or more of a diagnostic cardiac catheterization, percutaneouscoronary intervention (balloon angioplasty with or without stentplacement), coronary artery bypass graft (CABG), and administration ofpharmacologic agents selected from nitrates, beta blockers, ACEinhibitor and lipid-lowering agents.

Further still, in certain related embodiments, upon making a negativediagnosis for obstructive coronary artery disease in the subjectaccording to the methods disclosed herein, a medical practitioner canadvantageously use the information thereby obtained to identify the needfor an intervention in the subject, such as an intervention selectedfrom one or more of ongoing monitoring and management of coronary riskfactors including hypertension, diabetes, and smoking, and lifestylemodifications selected from diet modification, exercise and smokingcessation.

In still other embodiments, upon making a positive prognosis of acardiac outcome (e.g., a prognosis of cardiovascular death, myocardialinfarct (MI), stroke, all cause death, or a composite thereof) accordingto the methods disclosed herein, a medical practitioner canadvantageously use the prognostic information thereby obtained toidentify the need for an intervention in the subject, such as anintervention selected from one or more of stress testing with ECGresponse or myocardial perfusion imaging, coronary computed tomographyangiogram, diagnostic cardiac catheterization, percutaneous coronary(e.g., balloon angioplasty with or without stent placement), coronaryartery bypass graft (CABG), enrollment in a clinical trial, andadministration or monitoring of effects of agents selected from, but notlimited to, of agents selected from nitrates, beta blockers, ACEinhibitors, antiplatelet agents and lipid-lowering agents.

In further related embodiments, upon making a negative prognosis of acardiac outcome according to the methods described herein, a medicalpractitioner can advantageously use the information thereby obtained toidentify the need for an intervention, particularly an interventionselected from one or more of ongoing monitoring and management ofcoronary risk factors including hypertension, diabetes, hyperlipidemiaand smoking; and lifestyle modifications selected from dietmodification, exercise and smoking cessation.

As used herein, a “biological sample” encompasses essentially any sampletype obtained from a subject that can be used in a diagnostic orprognostic method described herein. The biological sample may be anybodily fluid, tissue or any other sample from which clinically relevantbiomarker levels may be determined. The definition encompasses blood andother liquid samples of biological origin, solid tissue samples such asa biopsy specimen or tissue cultures or cells derived therefrom and theprogeny thereof. The definition also includes samples that have beenmanipulated in any way after their procurement, such as by treatmentwith reagents, solubilization, or enrichment for certain components,such as polynucleotides. The term “biological sample” encompasses aclinical sample, but also, in some instances, includes cells in culture,cell supernatants, cell lysates, blood, serum, plasma, urine, cerebralspinal fluid, biological fluid, and tissue samples. The sample may bepretreated as necessary by dilution in an appropriate buffer solution orconcentrated, if desired. Any of a number of standard aqueous buffersolutions, employing one of a variety of buffers, such as phosphate,Tris, or the like, preferably at physiological pH can be used.Biological samples can be derived from patients using well knowntechniques such as venipuncture, lumbar puncture, fluid sample such assaliva or urine, or tissue biopsy and the like.

In certain specific embodiments, the biological sample used in themethods of the present disclosure include but are not limited to, wholeblood, plasma, serum, or urine. In some embodiments, the sample is wholeblood. In some embodiments, the sample is plasma. In other embodiments,the sample is serum or urine.

Determining biomarker levels in a sample taken from a subject can beaccomplished according to standard techniques known and available to theskilled artisan. In many instances, this will involve carrying outprotein detection methods, which provide a quantitative measure ofprotein biomarkers present in a biological sample.

Many embodiments of the present disclosure are based, in part, on theuse of binding agents that specifically bind to the biomarkers describedherein and thereby allow for a determination of the levels of thebiomarkers in a biological sample. Any of a variety of binding agentsmay be used including, for example, antibodies, polypeptides, sugars andnucleic acids.

In a specific embodiment of the present disclosure, the binding agent isan antibody or a fragment thereof that specifically binds to a biomarkerof the present disclosure, and that is effective to determine the levelof the biomarker to which it binds in a biological sample.

The term “specifically binds” or “binds specifically,” in the context ofbinding interactions between two molecules, refers to high avidityand/or high affinity binding of an antibody (or other binding agent) toa specific polypeptide subsequence or epitope of a biomarker. Antibodybinding to an epitope on a specific biomarker sequence (also referred toherein as “an epitope”) is preferably stronger than binding of the sameantibody to any other epitope, particularly those which may be presentin molecules in association with, or in the same sample, as the specificbiomarker of interest. Antibodies which bind specifically to a biomarkerof interest may be capable of binding other polypeptides at a weak, yetdetectable, level (e.g., 10% or less, 5% or less, 1% or less of thebinding shown to the polypeptide of interest). Such weak binding, orbackground binding, is readily discernible from the specific antibodybinding to the compound or polypeptide of interest, e.g. by use ofappropriate controls. In general, antibodies used in compositions andmethods of the invention which bind to a specific biomarker protein witha binding affinity of 10′ moles/L or more, preferably 10⁸ moles/L ormore are said to bind specifically to the specific biomarker protein.

In one embodiment, the affinity of specific binding of an antibody orother binding agent to a biomarker is about 2 times greater thanbackground binding, about 5 times greater than background binding, about10 times greater than background binding, about 20 times greater thanbackground binding, about 50 times greater than background binding,about 100 times greater than background binding, or about 1000 timesgreater than background binding, or more.

In another embodiment, the affinity of specific binding of an antibodyor other binding agent to a biomarker is between about 2 to about 1000times greater than background binding, between about 2 to 500 timesgreater than background binding, between about 2 to about 100 timesgreater than background binding, between about 2 to about 50 timesgreater than background binding, between about 2 to about 20 timesgreater than background binding, between about 2 to about 10 timesgreater than background binding, or any intervening range of affinity.

The term “antibody” herein is used in the broadest sense andspecifically covers, but is not limited to, monoclonal antibodies,polyclonal antibodies, multispecific antibodies (e.g., bispecificantibodies) formed from at least two intact antibodies, single chainantibodies (e.g., scFv), and antibody fragments or other derivatives, solong as they exhibit the desired biological specificity.

The term “monoclonal antibody” as used herein refers to an antibodyobtained from a population of substantially homogeneous antibodies,i.e., the individual antibodies comprising the population are identicalexcept for possible naturally occurring mutations that can be present inminor amounts. In certain specific embodiments, the monoclonal antibodyis an antibody specific for a biomarker described herein.

Monoclonal antibodies are highly specific, being directed against asingle antigenic site. Furthermore, in contrast to conventional(polyclonal) antibody preparations which typically include differentantibodies directed against different determinants (epitopes), eachmonoclonal antibody is directed against a single determinant on theantigen. In addition to their specificity, the monoclonal antibodies areadvantageous in that they are synthesized by the hybridoma culture,uncontaminated by other immunoglobulins. The modifier “monoclonal”indicates the character of the antibody as being obtained from asubstantially homogeneous population of antibodies, and is not to beconstrued as requiring production of the antibody by any particularmethod. For example, the monoclonal antibodies to be used in accordancewith the present invention may be made by the hybridoma method firstdescribed by Kohler et al., Nature, 256: 495 (1975), or may be made byrecombinant DNA methods (see, e.g., U.S. Pat. No. 4,816,567), or anyother suitable methodology known and available in the art. The“monoclonal antibodies” may also be isolated from phage antibodylibraries using the techniques described in Clackson et al., Nature,352: 624-628 (1991) and Marks et al., J. Mol. Biol., 222: 581-597(1991), for example.

The monoclonal antibodies herein specifically include “chimeric”antibodies in which a portion of the heavy and/or light chain isidentical with or homologous to corresponding sequences in antibodiesderived from a particular species or belonging to a particular antibodyclass or subclass, while the remainder of the chain (s) is identicalwith or homologous to corresponding sequences in antibodies derived fromanother species or belonging to another antibody class or subclass, aswell as fragments of such antibodies, so long as they exhibit thedesired biological activity and/or specificity (e.g., U.S. Pat. No.4,816,567; Morrison et al., Proc. Natl. Acad. Sci. USA, 81: 6851-6855(1984)). Methods of making chimeric antibodies are known in the art.

“Functional fragments” of antibodies can also be used and include thosefragments that retain sufficient binding affinity and specificity for abiomarker so as to permit a determination of the level of the biomarkerin a biological sample. In some cases, a functional fragment will bindto a biomarker with substantially the same affinity and/or specificityas an intact full chain molecule from which it may have been derived.

An “isolated” antibody is one which has been identified and separatedand/or recovered from a component of its natural environment.Contaminant components of its natural environment are materials thatwould interfere with diagnostic or prognostic uses for the antibody, andmay include enzymes, hormones, and other proteinaceous ornon-proteinaceous solutes. In specific embodiments, the antibody will bepurified to greater than 95% by weight of antibody, e.g., as determinedby the Lowry method, and most preferably more than 99% by weight.

The terms “detectably labeled antibody” refers to an antibody (orantibody fragment) which retains binding specificity for a biomarkerdescribed herein, and which has an attached detectable label. Thedetectable label can be attached by any suitable means, e.g., bychemical conjugation or genetic engineering techniques. Methods forproduction of detectably labeled proteins are well known in the art.Detectable labels may be selected from a variety of such labels known inthe art, including, but not limited to, haptens, radioisotopes,fluorophores, paramagnetic labels, enzymes (e.g., horseradishperoxidase), or other moieties or compounds which either emit adetectable signal (e.g., radioactivity, fluorescence, color) or emit adetectable signal after exposure of the label to its substrate. Variousdetectable label/substrate pairs (e.g., horseradishperoxidase/diaminobenzidine, avidin/streptavidin,luciferase/luciferin)), methods for labeling antibodies, and methods forusing labeled antibodies are well known in the art (see, for example,Harlow and Lane, eds. (Antibodies: A Laboratory Manual (1988) ColdSpring Harbor Laboratory Press, Cold Spring Harbor, N.Y.)).

In certain particular embodiments, the level of a protein biomarker ofthe present disclosure is determined using an assay or format including,but not limited to, e.g., immunoassays, ELISA sandwich assays, lateralflow assays, flow cytometry, mass spectrometric detection, calorimetricassays, binding to a protein array (e.g., antibody array), singlemolecule detection methods, nanotechnology-based detection methods, orfluorescent activated cell sorting (FACS). In some embodiments, anapproach involves the use of labeled affinity reagents (e.g.,antibodies, small molecules, etc.) that recognize epitopes of one ormore biomarker proteins in an ELISA, antibody-labelled fluorescent beadarray, antibody array, or FACS screen. As noted, any of a number ofillustrative methods for producing, evaluating and/or using antibodiesfor detecting and quantifying the biomarkers herein are well known andavailable in the art. It will also be understood that the proteindetection and quantification in accordance with the methods describedherein can be carried out in single assay format, multiplex format, orother known formats.

A number of suitable high-throughput multiplex formats exist forevaluating the disclosed biomarkers. Typically, the term“high-throughput” refers to a format that performs a large number ofassays per day, such as at least 100 assays, 1000 assays, up to as manyas 10,000 assays or more per day. When enumerating assays, either thenumber of samples or the number of markers assayed can be considered.

In some embodiments of the present invention, the samples are analyzedon an assay instrument. For example, the assay instrument may be amultiplex analyzer that simultaneously measures multiple analytes, e.g.proteins, in a single microplate well. The assay format may bereceptor-ligand assays, immunoassays, and enzymatic assays. An exampleof such an analyzer is the Luminex® 100/200 system which is acombination of three xMAP® Technologies. The first is xMAP microspheres,a family of fluorescently dyed micron-sized polystyrene microspheresthat act as both the identifier and the solid surface to build theassay. The second is a flow cytometry-based instrument, the Luminex®100/200 analyzer, which integrates key xMAP® detection components, suchas lasers, optics, fluidics, and high-speed digital signal processors.The third component is the xPONENT® software, which is designed forprotocol-based data acquisition with robust data regression analysis.

By determining biomarker levels and optionally clinical variable statusfor a subject, a dataset may be generated and used (as further describedherein) to classify the biological sample to one or more of riskstratification, prognosis, diagnosis, and monitoring of thecardiovascular status of the subject, and further assigning a likelihoodof a positive or negative diagnosis, outcome, or one or more futurechanges in cardiovascular status to the subject to thereby establish adiagnosis and/or prognosis of cardiovascular disease and/or outcome, asdescribed herein. Of course, the dataset may be obtained via automationor manual methods.

Statistical Analysis

By analyzing combinations of biomarkers and optionally clinicalvariables as described herein, the methods of the invention are capableof discriminating between different endpoints. The endpoints mayinclude, for example, obstructive coronary artery disease (CAD),cardiovascular death (CVD), myocardial infarction (MI), stroke,composites thereof, and composites further of all cause death. Theidentity of the markers and their corresponding features (e.g.,concentration, quantitative levels) are used in developing andimplementing an analytical process, or plurality of analyticalprocesses, that discriminate between clinically relevant classes ofpatients.

A biomarker and clinical variable dataset may be used in an analyticprocess for correlating the assay result(s) generated by the assayinstrument and optionally the clinical variable status to thecardiovascular status of the subject, wherein said correlation stepcomprises correlating the assay result(s) to one or more of riskstratification, prognosis, diagnosis, classifying and monitoring of thecardiovascular status of the subject, wherein said correlating stepcomprises assigning a likelihood of a positive or negative diagnosis, orone or more future changes in cardiovascular status to the subject basedon the assay result(s).

A biomarker and clinical variable dataset may be used in an analyticprocess for generating a diagnostic and/or prognostic result or score.For example, an illustrative analytic process can comprise a linearmodel with one term for each component (protein level or clinicalfactor). The result of the model is a number that generates a diagnosisand/or prognosis. The result may also provide a multi-level orcontinuous score with a higher number representing a higher likelihoodof disease or risk of event.

The examples below illustrate how data analysis algorithms can be usedto construct a number of such analytical processes. Each of the dataanalysis algorithms described in the examples use features (e.g.,quantitative protein levels and/or clinical factors) of a subset of themarkers identified herein across a training population. Specific dataanalysis algorithms for building an analytical process or plurality ofanalytical processes, that discriminate between subjects disclosedherein will be described in the subsections below. Once an analyticalprocess has been built using these exemplary data analysis algorithms orother techniques known in the art, the analytical process can be used toclassify a test subject into one of the two or more phenotypic classesand/or predict survival/mortality or a severe medical event within aspecified period of time after the blood test is obtained. This isaccomplished by applying one or more analytical processes to one or moremarker profile(s) obtained from the test subject. Such analyticalprocesses, therefore, have enormous value as diagnostic or prognosticindicators.

The present invention therefore further provides for an algorithm thatmay be used to transform the levels of a panel of biomarkers, asdescribed above, into a score that may be used to determine whether apatient is diagnosed with obstructive coronary artery disease or has aprognosis of risk for developing an adverse cardiovascular event.

The data are processed prior to the analytical process. The data in eachdataset are collected by measuring the values for each marker, usuallyin duplicate or triplicate or in multiple replicates. The data may bemanipulated; for example, raw data may be transformed using standardcurves, and the average of replicate measurements used to calculate theaverage and standard deviation for each patient. These values may betransformed before being used in the models, e.g., log-transformed,normalized to a standard scale, Winsorized, etc. The data is transformedvia computer software. This data can then be input into the analyticalprocess with defined parameters.

The direct levels of the proteins (after log-transformation andnormalization), the presence/absence of clinical factors represented inbinary form (e.g. sex), and/or clinical factors in quantitative form(e.g., BMI, age) provide values that are plugged into the diagnosticmodel provided by the software, and the result is evaluated against oneor more cutoffs to determine the diagnosis or prognosis.

The following are examples of the types of statistical analysis methodsthat are available to one of skill in the art to aid in the practice ofthe disclosed methods, panels, assays, and kits. The statisticalanalysis may be applied for one or both of two tasks. First, these andother statistical methods may be used to identify preferred subsets ofmarkers and other indices that will form a preferred dataset. Inaddition, these and other statistical methods may be used to generatethe analytical process that will be used with the dataset to generatethe result. Several statistical methods presented herein or otherwiseavailable in the art will perform both of these tasks and yield a modelthat is suitable for use as an analytical process for the practice ofthe methods disclosed herein.

Prior to analysis, the data is partitioned into a training set and avalidation set. The training set is used to train, evaluate and buildthe final diagnostic or prognostic model. The validation set is not usedat all during the training process, and is only used to validate finaldiagnostic or prognostic models. All processes below, except whenexplicitly mentioned, involve the use of only the training set.

The creation of training and validation sets is done through randomselection. After these sets are determined, the balance of variousoutcomes (e.g., presence of 70% or greater obstruction, the occurrenceof MI within one year, etc.) is considered to confirm that the outcomesof interest are properly represented in each data set.

The features (e.g., proteins and/or clinical factors) of the diagnosticand/or prognostic models are selected for each outcome using acombination of analytic processes, including least angle regression(LARS; a procedure based on stepwise forward selection), shrinkage instatistical learning methods such as least absolute shrinkage andselection operator (LASSO), significance testing, and expert opinion.

The statistical learning method used to generate a result(classification, survival/mortality within a specified time, etc.) maybe any type of process capable of providing a result useful forclassifying a sample (e.g., a linear model, a probabilistic model, adecision tree algorithm, or a comparison of the obtained dataset with areference dataset).

The diagnostic or prognostic signal in the features is evaluated withthese statistical learning methods using a cross-validation procedure.For each cross-validation fold, the training set is further split intotraining and validation sets (hereby called CV-training andCV-validation data sets).

For each fold of cross validation, the diagnostic or prognostic model isbuilt using the CV-training data, and evaluated with the CV-validationdata.

Models during the cross-validation process are evaluated with standardmetrics of classification accuracy, e.g. the area under the ROC curve(AUC), sensitivity (Sn), specificity (Sp), positive predictive values(PPV), and negative predictive values (NPV).

Once a set of features (e.g. quantitative protein levels and optionallyclinical factors) are selected to compose a final diagnostic orprognostic panel, a final predictive model is built using all of thetraining data.

Applying the patient data (e.g., quantitative protein levels and/orclinical factors) into the final predictive model yields aclassification result. These results can be compared against a thresholdfor classifying a sample within a certain class (e.g., positive ornegative diagnosis and/or prognosis, or a severity/likelihood score).

Final models are evaluated with the validation data set. To respect theauthority of the validation data set, it is not used in an iterativeway, to feed information back into the training process. It is only usedas the full stop of the analytic pipeline.

Models are evaluated with the validation data set using metrics ofaccuracy, including the AUC, sensitivity, specificity, positivepredictive value and/or negative predictive value. Other metrics ofaccuracy, such as hazard ratio, relative risk, and net reclassificationindex are considered separately for models of interest.

This final model or a model optimized for a particular biomarkerplatform, when used in a clinical setting, may be implemented as asoftware system, either running directly on the assay hardware platformor an independent system. The model may receive protein level orconcentration data directly from the assay platform or other means ofdata transfer, and patient clinical data may be received via electronic,manual, or other query of patient medical records or through interactiveinput with the operator. This patient data may be processed and runthrough the final model, which will provide a result to clinicians andmedical staff for purposes of decision support.

Panels, Assays, and Kits

The present invention further provides panels, assays, and kitscomprising at least 1, at least 2, at least 3, at least 4 or greaterthan 4 biomarkers and/or clinical variable(s), in order to aid orfacilitate a diagnostic or prognostic finding according to the presentdisclosure. For example, in some embodiments, a diagnostic or prognosticpanel or kit comprises one or a plurality of biomarkers set out inTables 1A, 1B, 2A, and 2B and optionally one or a plurality ofapplicable clinical variables set out in Tables 3A, 3B, 4A, and 4B.

It will be understood that, in many embodiments, the panels, assays, andkits described herein comprise antibodies, binding fragments thereofand/or other types of binding agents which are specific for thebiomarkers of Tables 1A, 1B, 2A, and 2B, and which are useful fordetermining the levels of the corresponding biomarker in a biologicalsample according to the methods describe herein. Accordingly, in eachdescription herein of a panel, assay, or kit comprising one or aplurality of biomarkers, it will be understood that the very same panel,assay, or kit can advantageously comprise, in addition or instead, oneor a plurality of antibodies, binding fragments thereof or other typesof binding agents, which are specific for the biomarkers of Tables 1A,1B, 2A, and 2B. Of course, the panels, assays, and kits can furthercomprise, include or recommend a determination of one or a plurality ofapplicable clinical variables as set out in Tables 3A, 3B, 4A, and 4B.

In certain specific embodiments, the biomarkers and/or clinicalvariables used in in conjunction with a panel, assay, or kit includethose listed in Tables 1A, 1B, 2A, 2B and Tables 3A, 3B, 4A and 4B,respectively, particularly those which are associated with a p-value ofless than 0.1, less than 0.05, less than 0.01 or less than 0.001.

In some embodiments, panels, assays, and kits may comprise at least 1,at least 2, at least 3 or at least 4 biomarkers as described herein. Inother embodiments, the number of biomarkers employed can include atleast 5, 6, 7, 8, 9 or 10 or more. In still other embodiments, thenumber of biomarkers employed can include at least 15, 20, 25 or 50, ormore.

In addition to the biomarkers disclosed in Tables 1A, 1B, 2A, and 2B, apanel, assay, or kit may include additional biomarkers (or bindingagents thereto) not specifically described herein, particularly wherethe biomarkers in the panel themselves provide statistically significantinformation regarding a diagnosis or prognosis of interest, e.g.,whether a patient has coronary artery disease or would be at increasedrisk for cardiovascular disease and/or an adverse cardiovascular eventwithin one year, at one year, or beyond one year.

As described herein, panels, assays, and kits of the present disclosurecan be used for identifying the presence of cardiovascular disease in asubject, particularly the presence of obstructive coronary arterydisease and/or for predicting cardiac events. In some embodiments, adiagnostic panel, assay, or kit identifies in a subject the presence of70% or greater obstruction in any major epicardial vessels.

In other embodiments, a prognostic panel, assay, or kit is used topredict the risk of a cardiovascular disease or event within one year,about 1 year, about 2 years, about 3 years, about 4 years, about 5years, or more from the date on which the sample is drawn. Timeendpoints are defined as from sample draw and include less than oneyear, one year, and greater than one year. Less than or within one yearmay be any time from time of sample draw up to and including 365 days.For example, the panel results may predict the risk of a cardiovasculardisease or event from time of sample draw to 30 days, to 60 days, to 90days, to 120 days, to 150 days, to 180 days, to 210 days, to 240 days,to 270 days, to 300 days, to 330 days, to 360 days, to 365 days. In yetother embodiments, time endpoints are defined as 3 days post sample drawto 30 days, to 60 days, to 90 days, to 120 days, to 150 days, to 180days, to 210 days, to 240 days, to 270 days, to 300 days, to 330 days,to 360 days, to 365 days.

In specific embodiments, panels, assays, and kits for the diagnosis ofobstructive CAD comprise at least 1, at least 2, at least 3, at least 4or greater than four biomarkers, or antibodies, binding fragmentsthereof or other types of binding agents, which are specific for thebiomarkers, where the biomarkers are selected from the group consistingof adiponectin, apolipoprotein C-I, decorin, interleukin-8, kidneyinjury molecule-1, matrix metalloproteinase 9, midkine, myoglobin,pulmonary surfactant associated protein D, stem cell factor, andtroponin. In some embodiments, at least one clinical variable describedherein is used in conjunction with the biomarker levels determined. Inother embodiments, the clinical variable is selected from the groupconsisting of age, history of coronary artery bypass graft surgery(CABG), history of diabetes type 2, history of hemodialysis, history ofmyocardial infarct (MI), history of percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), and sex.

In specific embodiments, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-1, kidney injurymolecule-1, and midkine and clinical variables of history ofpercutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement), and sex. This combination of biomarkers andclinical variables is represented by panel FM139/685 in Table 25,Example 1, and FIGS. 1-4.

In specific embodiments, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-1, kidney injurymolecule-1, and midkine and clinical variables of history ofpercutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement), sex, and age. This combination of biomarkersand clinical variables is represented by panel FM144/696 in Table 25 andFIG. 5.

In another embodiment, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-I, kidney injurymolecule-1, and midkine and clinical variables of sex and age. Thiscombination of biomarkers and clinical variables is represented by panelFM145/701 in Table 25 and FIG. 6.

In another embodiment, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-I, kidney injurymolecule-1, and midkine. This combination of biomarkers is representedby panel FM146/690 in Table 25 and FIG. 7.

In another embodiment, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, kidney injury molecule-1, and midkine andclinical variables of history of diabetes mellitus type 2, sex, and age.This combination of biomarkers and clinical variables is represented bypanel FM152/757 in Table 25 and FIG. 8.

Embodiments of the present invention comprise a panels, assays, and kitsfor the diagnosis of 70% or greater obstruction in any major epicardialvessel comprising at least one biomarker and one or more clinicalvariables.

In a specific embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprises abiomarker for midkine and clinical variables of history of percutaneouscoronary intervention (e.g., balloon angioplasty with or without stentplacement) and sex. This combination of biomarkers and clinicalvariables is represented by panel FM117a/657 in Table 25 and FIG. 9.

In a specific embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprises abiomarker for adiponectin and clinical variables of history ofpercutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement) and sex. This combination of biomarkers andclinical variables is represented by panel FM139CLa/658 in Table 25 andFIG. 10.

In a specific embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprises abiomarker for apolipoprotein C-1 and clinical variables of history ofpercutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement) and sex. This combination of biomarkers andclinical variables is represented by panel FM139CLb/750 in Table 25 andFIG. 11.

In a specific embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprises abiomarker for kidney injury molecule-1 and clinical variables of historyof percutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement) and sex. This combination of biomarkers andclinical variables is represented by panel FM139CLc/751 in Table 25 andFIG. 12.

In yet other embodiments, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin and midkine and clinical variables of historyof percutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement) and sex. This combination of biomarkers andclinical variables is represented by panel FM117b/663 in Table 25 andFIG. 13.

In another embodiment, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for apolipoprotein C-I and midkine and clinical variables ofhistory of percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement) and sex. This combination of biomarkersand clinical variables is represented by panel FM139CLd/752 in Table 25and FIG. 14.

In another embodiment, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for kidney injury molecule-1 and midkine and clinicalvariables of history of percutaneous coronary intervention (e.g.,balloon angioplasty with or without stent placement), and sex. Thiscombination of biomarkers and clinical variables is represented by panelFM139Cle/753 in Table 25 and FIG. 15.

In a further embodiment, a panel, assay, or kit for the diagnosis of 70%or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-I, and midkine and clinicalvariables of history of percutaneous coronary intervention (e.g.,balloon angioplasty with or without stent placement) and sex. Thiscombination of biomarkers and clinical variables is represented by panelFM139CLf/754 in Table 25 and FIG. 16.

In yet another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, kidney injury molecule-1, and midkine andclinical variables of history of percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), and sex.This combination of biomarkers and clinical variables is represented bypanel FM139CLg/755 in Table 25 and FIG. 17.

In yet another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, decorin, and midkine and clinical variablesof history of myocardial infarct (MI), history of percutaneous coronaryintervention (e.g., balloon angioplasty with or without stentplacement), and sex. This combination of biomarkers and clinicalvariables is represented by panel FM46/572 in Table 25, Example 2, andFIGS. 18-20.

In yet another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin and midkine and clinical variables of historyof myocardial infarct (MI), history of percutaneous coronaryintervention (e.g. balloon angioplasty with or without stent placement),and sex. This combination of biomarkers and clinical variables isrepresented by panel FM46Fd/586 in Table 25 and FIG. 21.

In yet another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for decorin and midkine and clinical variables of history ofmyocardial infarct (MI), history of percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), and sex.This combination of biomarkers and clinical variables is represented bypanel FM46Fe/587 in Table 25 and FIG. 22.

In yet another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin and decorin and clinical variables of historyof myocardial infarct (MI), history of percutaneous coronaryintervention (e.g., balloon angioplasty with or without stentplacement), and sex. This combination of biomarkers and clinicalvariables is represented by panel FM46Ff/588 in Table 25 and FIG. 23.

In still another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, interleukin-8, kidney injury molecule-1, andstem cell factor and clinical variables of history of percutaneouscoronary intervention (e.g., balloon angioplasty with or without stentplacement), sex, and age. This combination of biomarkers and clinicalvariables is represented by panel FM186/796 in Table 25 and FIG. 24.

In still another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, interleukin-8, kidney injury molecule-1, andstem cell factor and clinical variables of history of percutaneouscoronary intervention (e.g., balloon angioplasty with or without stentplacement) and sex. This combination of biomarkers and clinicalvariables is represented by panel FM189/798 in Table 25 and FIG. 25.

In still another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-1, interleukin-8, kidneyinjury molecule-1, and stem cell factor and clinical variables ofhistory of percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement), sex, and age. This combination ofbiomarkers and clinical variable sis represented by panel FM187/792 inTable 25 and FIG. 26.

In still another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-1, interleukin-8, kidneyinjury molecule-1, and stem cell factor and clinical variables ofhistory of percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement), and sex. This combination ofbiomarkers and clinical variables is represented by panel FM188/794 inTable 25 and FIG. 27.

In still another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, apolipoprotein C-1, matrix metalloproteinase9, midkine, myoglobin, and pulmonary surfactant associated protein D andclinical variables of history of coronary artery bypass graft surgery(CABG), history of percutaneous coronary intervention (e.g., balloonangioplasty with or without stent placement), and sex. This combinationof biomarkers and clinical variables is represented by panel FM02/410 inTable 25, Example 3, and FIG. 28.

In still another embodiment, a panel, assay, or kit for the diagnosis of70% or greater obstruction in any major epicardial vessel comprisesbiomarkers for adiponectin, midkine, pulmonary surfactant associatedprotein D, and troponin and clinical variables of history of coronaryartery bypass graft surgery (CABG), history of hemodialysis, history ofmyocardial infarct, and sex. This combination of biomarkers and clinicalvariables is represented by panel FM01/390 in Table 25 and FIG. 29.

Embodiments of the present invention also provide panels, assays, andkits for the prognosis of composite cardiovascular death, myocardialinfarction or stroke, where the panels comprise one or more biomarkersor antibodies, binding fragments thereof or other types of bindingagents, which are specific for the biomarkers disclosed herein. Suchpanels, assays, and kits can be used, for example, for determining aprognosis of the risk of a composite cardiovascular death, myocardialinfarction or stroke within a specified time in the subject, such aswithin one year, or within three years. In some embodiments, the timeendpoint is defined as starting from sample draw. In other embodiments,the time endpoint is defined as starting from three (3) days post sampledraw.

In certain specific embodiments, a panel, assay, or kit for theprognosis of a composite cardiovascular death, myocardial infarction orstroke comprises the biomarkers kidney injurymolecule-1, N terminalprohormone of brain natriuretic protein (NT-proBNP), osteopontin, andtissue inhibitor of metalloproteinases-1 (TIMP-1). In some embodiments,the time endpoint is defined as starting from three (3) days post sampledraw. This combination of biomarkers is represented by panel FM160/02 inTable 25, Example 4, and FIG. 30.

In certain specific embodiments, a panel, assay, or kit for theprognosis of a composite cardiovascular death, myocardial infarction orstroke comprises the biomarkers N terminal prohormone of brainnatriuretic protein (NT-proBNP), osteopontin, and tissue inhibitor ofmetalloproteinases-1 (TIMP-1). In some embodiments, the time endpoint isdefined as starting from sample draw (as described by panel FM96/04 inTable 25, Example 5, and FIG. 31). In other embodiments the timeendpoint is defined as starting from three (3) days post sample draw.This combination of biomarkers is represented by panel FM 190/33 inTable 25 and FIG. 32.

In another specific embodiment, a panel, assay, or kit for the prognosisof a composite cardiovascular death, myocardial infarction or strokecomprises at least NT-proBNP and osteopontin. In some embodiments, thetime endpoint is defined as starting from sample draw. This combinationof biomarkers is represented by panel FM98/03 in Table 25 and FIG. 33.

Embodiments of the present invention also provide panels, assays, andkits for the prognosis of a composite endpoint of all-cause death,myocardial infarction or stroke, where the panels comprise one or morebiomarkers or antibodies, binding fragments thereof or other types ofbinding agents, which are specific for the biomarkers disclosed herein.Such panels, assays, and kits can be used, for example, for determininga prognosis of the risk of a composite endpoint of all-cause death,myocardial infarction or stroke, within a specified time in the subject,such as within one year, or within three years. In some embodiments, thetime endpoint is defined as starting from sample draw. In otherembodiments, the time endpoint is defined as starting from three (3)days post sample draw.

In certain specific embodiments, a panel, assay, or kit for theprognosis of a composite endpoint of all-cause death, myocardialinfarction or stroke comprises biomarkers for kidney injury molecule-1,N terminal prohormone of brain natriuretic protein (NT-proBNP),osteopontin, and tissue inhibitor of metalloproteinases-1 (TIMP-1). Insome embodiments, the time endpoint is defined as starting from three(3) days post sample draw. This combination of biomarkers is representedby panel FM209/02 in Table 25 and FIG. 34.

In one specific embodiment, a panel, assay, or kit for the prognosis ofa composite endpoint of all-cause death, myocardial infarction or strokecomprises N terminal prohormone of brain natriuretic protein(NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinases-1(TIMP-1). In some embodiments, the time endpoint is defined as startingfrom sample draw. (As described by panel FM111/05 in Table 25 and FIG.35). In other embodiments, the time endpoint is defined as starting fromthree (3) days post sample draw. This combination of biomarkers isrepresented by panel FM210/03 in Table 25 and FIG. 36.

In another specific embodiment, a panel, assay, or kit for the prognosisof a composite endpoint of all-cause death, myocardial infarction orstroke comprises NT-proBNP and osteopontin. In some embodiments, thetime endpoint is defined as starting from sample draw. This combinationof biomarkers and clinical variables is represented by panel FM110/04 inTable 25 and FIG. 37.

Embodiments of the present invention also provide panels, assays, andkits for the prognosis of a composite endpoint of cardiovascular deathor myocardial infarction, wherein the panels, assays, and kits compriseone or more biomarkers or antibodies, binding fragments thereof or othertypes of binding agents, which are specific for the biomarkers disclosedherein. Such panels, assays, and kits can be used, for example, fordetermining a prognosis of cardiovascular death or myocardial infarctionwithin a specified time in the subject, such as within one year, orwithin three years. In some embodiments, the time endpoint is defined asstarting from sample draw. In other embodiments, the time endpoint isdefined as starting from three (3) days post sample draw.

In certain specific embodiments, a panel, assay, or kit for theprognosis of a composite endpoint of cardiovascular death or myocardialinfarction comprises biomarkers for apolipoprotein A-II, N terminalprohormone of brain natriuretic protein (NT-proBNP), and osteopontin. Insome embodiments, the time endpoint is defined as starting from three(3) days post sample draw. This combination of biomarkers is representedby panel FM211/03 in Table 25 and FIG. 38.

In other specific embodiments, a panel, assay, or kit for the prognosisof a composite endpoint of cardiovascular death or myocardial infarctioncomprises biomarkers for apolipoprotein A-II, midkine, N terminalprohormone of brain natriuretic protein (NT-proBNP), and osteopontin. Insome embodiments, the time endpoint is defined as starting from sampledraw (as described by panel FM77/26 in Table 25 and FIG. 39). In otherembodiments, the time endpoint is defined as starting from three (3)days post sample draw. This combination of biomarkers is represented bypanel FM212/02 in Table 25 and FIG. 40.

Embodiments of the present invention also provide panels, assays, andkits for the prognosis of myocardial infarct (MI), wherein the panelscomprise one or more biomarkers or antibodies, binding fragments thereofor other types of binding agents, which are specific for the biomarkersdisclosed herein. These can be used, for example, for determining aprognosis of risk of myocardial infarction within a specified time inthe subject, such as within one year, or within three years. In someembodiments, the time endpoint is defined as starting from sample draw.In other embodiments, the time endpoint is defined as starting fromthree (3) days post sample draw.

In certain specific embodiments, a panel, assay, or kit for theprognosis of myocardial infarct comprises biomarkers for N terminalprohormone of brain natriuretic protein (NT-proBNP) and osteopontin. Insome embodiments, the time endpoint is defined as starting from three(3) days post sample draw. This combination of biomarkers is representedby panel FM201/MI002 in Table 25 and FIG. 41.

In certain specific embodiments, a panel, assay, or kit for theprognosis of myocardial infarct comprises biomarkers for N terminalprohormone of brain natriuretic protein (NT-proBNP), osteopontin, andvascular cell adhesion molecule. In some embodiments, the time endpointis defined as starting from three (3) days post sample draw. Thiscombination of biomarkers is represented by panel FM204/MI003 in Table25 and FIG. 42.

In certain specific embodiments, a panel, assay, or kit for theprognosis of myocardial infarct comprises biomarkers for kidney injurymolecule-1, N terminal prohormone of brain natriuretic protein(NT-proBNP), and vascular cell adhesion molecule. In some embodiments,the time endpoint is defined as starting from three (3) days post sampledraw. This combination of biomarkers is represented by panel FM202/MI005in Table 25 and FIG. 43.

In certain specific embodiments, a panel, assay, or kit for theprognosis of myocardial infarct comprises biomarkers for kidney injurymolecule-1, N terminal prohormone of brain natriuretic protein(NT-proBNP), and osteopontin. In some embodiments, the time endpoint isdefined as starting from three (3) days post sample draw. Thiscombination of biomarkers is represented by panel FM205/MI007 in Table25 and FIG. 44.

In certain specific embodiments, a panel, assay, or kit for theprognosis of myocardial infarct comprises biomarkers for N terminalprohormone of brain natriuretic protein (NT-proBNP), and. In someembodiments, the time endpoint is defined as starting from sample draw.This combination of biomarkers is represented by panel FM63/64 in Table25 and FIG. 45.

Embodiments of the present invention further provide panels, assays, andkits for prognosis of cardiovascular death, wherein the panels compriseone or more biomarkers, or antibodies, binding fragments thereof orother types of binding agents, which are specific for the biomarkersdisclosed herein. Such panels, assays, and kits can be used, forexample, for determining a prognosis of cardiovascular death within aspecified time in the subject, such as within one year, or within threeyears. In some embodiments, the time endpoint is defined as startingfrom sample draw. In other embodiments, the time endpoint is defined asstarting from three (3) days post sample draw. In some embodiments, atleast one clinical variable described herein is used in conjunction withthe biomarker levels determined. In a specific embodiment, the clinicalvariable is history of diabetes mellitus type 2.

In certain specific embodiments, a panel, assay, or kit for theprognosis of cardiovascular death comprises biomarkers forapolipoprotein A-II and osteopontin. In some embodiments, the timeendpoint is defined as starting from sample draw. In some embodiments,the time endpoint is defined as starting from sample draw (as describedby panel FM52/244 in Table 25 and FIG. 46). In some embodiments, thetime endpoint is defined as starting from three (3) days post sampledraw. This combination of biomarkers is represented by panelFM194/CVD001 in Table 25 and FIG. 47. In a specific embodiment, the timeendpoint is defined as starting from three (3) days post sample draw andfurther comprises at clinical variable is history of diabetes mellitustype 2. This combination of biomarkers and clinical variables isrepresented by panel FM193/R08 in Table 25 and FIG. 48.

In certain specific embodiments, a panel, assay, or kit for theprognosis of cardiovascular death comprises biomarkers forapolipoprotein A-II, midkine, and osteopontin. In some embodiments, thetime endpoint is defined as starting from sample draw. This combinationof biomarkers is represented by panel FM53/237 in Table 25 and FIG. 49.In some embodiments, the time endpoint is defined as starting from three(3) days post sample draw. This is represented by panel FM195/CVD002 inTable 25 and FIG. 50.

In certain specific embodiments, a panel, assay, or kit for theprognosis of cardiovascular death comprises biomarkers forapolipoprotein A-II, N terminal prohormone of brain natriuretic protein(NT-proBNP), osteopontin, and tissue inhibitor of metalloproteinases-1.In some embodiments, the time endpoint is defined as starting from three(3) days post sample draw. This combination of biomarkers is representedby panel FM207/R04 in Table 25 and FIG. 51.

In certain specific embodiments, a panel, assay, or kit for theprognosis of cardiovascular death comprises biomarkers for N terminalprohormone of brain natriuretic protein (NT-proBNP), osteopontin, andtissue inhibitor of metalloproteinases-1. In some embodiments, the timeendpoint is defined as starting from three (3) days post sample draw.This combination of biomarkers is represented by panel FM208/R05 inTable 25 and FIG. 52.

In certain embodiments, a panel, assay, or kit comprises at least 1, atleast 2, at least 3, at least 4 or greater than 4 antibodies or bindingfragments thereof, or other types of binding agents, where theantibodies, binding fragments or other binding agents are specific for abiomarker of Table 1A, 1B, 2A, and 2B.

It will be understood that the panels, assays, and kits of the presentdisclosure may further comprise virtually any other compounds,compositions, components, instructions, or the like, that may benecessary or desired in facilitating a determination of a diagnosis orprognosis according to the present disclosure. These may includeinstructions for using the panel, assay, or kit, instructions for makinga diagnostic or prognostic determination (e.g., by calculating adiagnostic or prognostic score), instructions or other recommendationsfor a medical practitioner in relation to preferred or desired modes oftherapeutic or diagnostic intervention in the subject in light of thediagnostic or prognostic determination, and the like.

In some embodiments, the panels, assays, and kits of the invention willfacilitate detection of the biomarkers discussed herein. Means formeasuring such blood, plasma and/or serum levels are known in the art,and include, for example, the use of an immunoassay. Standard techniquesthat may be used, for example, include enzyme-linked immunosorbent assay(“ELISA”) or Western blot.

In addition to the methods described above, any method known in the artfor quantitatively measuring levels of protein in a sample, e.g.,non-antibody-based methods, can be used in the methods and kits of theinvention. For example, mass spectrometry-based (such as, for example,Multiple Reaction Monitoring (MRM) mass spectrometry) or HPLC-basedmethods can be used. Methods of protein quantification are described in,for example, Ling-Na Zheng et al., 2011, J. of Analytical AtomicSpectrometry, 26, 1233-1236; Vaudel, M., et al., 2010, Proteomics, Vol.10: 4; Pan, S., 2009 J. Proteome Research, February; 8(2):787-97;Westermeier and Marouga, 2005, Bioscience Reports, Vol. 25, Nos. 1/2;Carr and Anderson, 2008, Clinical Chemistry. 54:1749-1752; and Aebersoldand Mann, 2003, Nature, Vol. 422.

Additionally, technologies such as those used in the field of proteomicsand other areas may also be embodied in methods, kits and other aspectsof the invention. Such technologies include, for example, the use ofmicro- and nano-fluidic chips, biosensors and other technologies asdescribed, for example, in United States Patent Application Nos.US2008/0202927; US2014/0256573; US2016/0153980; WO2016/001795;US2008/0185295; US2010/0047901; US2010/0231242; US2011/0154648;US2013/0306491; US2010/0329929; US2013/0261009; Sorger, 2008, NatureBiotechnol. 26:1345-1346; Li et al., 2002, Mol. Cell. Proteomics1.2:157; Hou et al., 2006, J. Proteome Res. 5(10):2754-2759; Li et al.,2001, Proteomics 1(8):975-986; Ramsey et al., 2003, Anal. Chem.75(15):3758-3764; Armenta et al., 2009, Electrophoresis 30(7):1145-1156; Lynch et al., 2004, Proteomics 4(6):1695-1702; Kingsmore etal., 2003, Curr. Opin. Biotechnol. 14(1):74-81).

EXAMPLES Example 1: A Clinical and Biomarker Scoring System to DiagnoseObstructive Coronary Artery Disease (CAD), Panel FM139/685

A convenience sample of 1251 patients undergoing coronary and/orperipheral angiography with or without intervention between 2008 and2011 were prospectively enrolled. Patients who received only aperipheral angiography or no catherization procedure at all wereexcluded from this analysis (N=244). Additionally, a chronologicalsubset of the final 153 patients who had received either a coronary orperipheral cath were withheld from this analysis, for their potentialuse in further validation of these models. Patients were referred forthese procedures for numerous reasons; this includes angiographyfollowing acute processes such as myocardial infarction (MI), unstableangina pectoris, and heart failure (HF), but also for non-acuteprocesses, such as for diagnostic evaluation of stable chest pain,failed stress testing, or pre-operatively prior to heart valve surgery.

After informed consent was obtained, detailed clinical and historicalvariables and reason for referral for angiography were recorded at thetime of the procedure. Results of coronary angiography were alsorecorded with highest percent stenosis within each major coronaryarteries or their branches. For the purposes of this analysis,significant coronary stenosis was characterized as ≥70% luminalobstruction.

Medical record review from time of enrollment to end of follow up wasundertaken. For identification of clinical end points, review of medicalrecords as well as phone follow up with patients and/or managingphysicians was performed. The Social Security Death Index and/orpostings of death announcements were used to confirm vital status. Thefollowing clinical end events were identified, adjudicated, and recordedby study investigators: death, non-fatal MI, HF, stroke, transientischemic attack, peripheral arterial complication and cardiacarrhythmia. For any recurring events, each discrete event was recorded.Additionally, deaths were adjudicated for presence/absence of acardiovascular cause.

Fifteen (15) mL of blood was obtained immediately before and immediatelyafter the angiographic procedure through a centrally-placed vascularaccess sheath. The blood was immediately centrifuged for 15 minutes,serum and plasma aliquoted on ice and frozen at −80° C. until biomarkermeasurement. Only the blood obtained immediately before the procedurewas used for this analysis.

After a single freeze-thaw cycle, 200 ul of plasma was analyzed for morethan 100 protein biomarkers on a Luminex 100/200 xMAP technologyplatform. This technology utilizes multiplexed, microsphere-based assaysin a single reaction vessel. It combines optical classification schemes,biochemical assays, flow cytometry and advanced digital signalprocessing hardware and software. Multiplexing is accomplished byassigning each protein-specific assay a microsphere set labeled with aunique fluorescence signature. An assay-specific capture antibody isconjugated covalently to each unique set of microspheres. Theassay-specific capture antibody on each microsphere binds the protein ofinterest. A cocktail of assay-specific, biotinylated detectingantibodies is reacted with the microsphere mixture, followed by astreptavidin-labeled fluorescent “reporter” molecule. Similar to a flowcytometer, as each individual microsphere passes through a series ofexcitation beams, it is analyzed for size, encoded fluorescencesignature and the amount of fluorescence generated is proportionate tothe protein level. A minimum of 100 individual microspheres from eachunique set are analyzed and the median value of the protein-specificfluorescence is logged. Using internal controls of known quantity,sensitive and quantitative results are achieved with precision enhancedby the analysis of 100 microspheres per data point.

The patients selected for analysis consisted of the chronologicallyinitial 927 patients who received a coronary angiogram. These includepatients who may have also received a peripheral angiogramconcomitantly.

The 927 patients selected for analysis were randomly split into atraining set (70%, or N=649) and a holdout validation set (30%, orN=278). Baseline clinical characteristics and protein concentrationsbetween those with and without ≥70% coronary stenosis in at least onemajor epicardial coronary artery were compared (Tables 1A, 3A, 5, and6); dichotomous variables were compared using two-sided Fishers exacttest, while continuous variables were compared using two-sidedtwo-sample T test. The biomarkers compared were tested with the WilcoxonRank Sum test, as their concentrations were not normally distributed.For any marker result that was unmeasurable, we utilized a standardapproach of imputing concentrations 50% below the limit of detection.

All work for biomarker selection and the development of a diagnosticmodel was done exclusively on the training set. The level orconcentration values for all proteins underwent the followingtransformation to facilitate the predictive analysis: (a) they werelog-transformed to achieve a normal distribution; (b) outliers wereclipped at the value of three times the median absolute deviation; and(c) the values were re-scaled to distribution with a zero mean and unitvariance. Candidate panels of proteins and clinical features wereselected via least angle regression (LARS), and models were generatedusing least absolute shrinkage and selection operator (LASSO) withlogistic regression, using Monte Carlo cross-validation with 400iterations. Candidates were subjected to further assessment ofdiscrimination via iterative model building, assessing change in areaunder the curve (AUC) with the addition of biomarkers to the base model,along with assessment of improvement in calibration from their additionthrough minimization of the Akaike or Bayesian Information Criteria(AIC, BIC) and goodness of fit in Hosmer-Lemeshow testing.

Once the final panel was selected, a final model was built with all ofthe training data. Multivariable logistic regression evaluated theperformance of the model in the training set as a whole as well as inseveral relevant subgroups, to determine how well the model performed inmen vs. women, in those who had a history of CAD vs. those who did nothave a history of CAD, and in those presenting with and without amyocardial infarct. Diagnostic odds ratios (OR) with 95% confidenceintervals (CI) were generated. Subsequently, the final model wasevaluated with the validation set: to do so, we generated a scoredistribution within the validation cohort, followed by receiver operatorcharacteristic (ROC) testing with valor of the score as a function ofthe AUC. Operating characteristics of the score were calculated, withsensitivity (Sn), specificity (Sp), positive and negative predictivevalue (PPV, NPV) generated. We also looked at methods for transformingthe single diagnostic score into levels of likelihood (e.g., afive-level score, where a score of 1 means that the patient is extremelyunlikely to have CAD, and a score of 5 means that the patient isextremely likely to have CAD), and evaluated each of these levels withthe above operating characteristics (FIG. 3 and Table 9). To evaluateprognostic meaning of the CAD score, we performed age- andCAD-score-adjusted Cox proportional hazards analyses to evaluate whethera score above the optimal threshold for CAD diagnosis also predictedfuture acute MI; hazard ratios (HR) for an elevated CAD score as well asper unit score increase with 95% CI were estimated. Lastly, time tofirst acute MI event as a function of elevated CAD score was calculated,displayed as Kaplan-Meier survival curves, and compared using log-ranktesting (FIG. 4).

All statistics were performed using R software, version 3.3 (RFoundation for Statistical Computing, Vienna, AT); p-values aretwo-sided, with a value <0.05 considered significant.

Table 1A shows biomarker concentrations and their diagnostic associationthat differ between those in the training set (N=649) with at least onecoronary artery stenosis ≥70% (N=428) and those who did not in thecohort of subjects who received a coronary cath, with or without anoptional peripheral cath.

Notably, of all the protein biomarkers (Table 1A) measured in thetraining set, those with severe CAD had lower concentrations ofadiponectin and apolipoprotein C-I (apo C-I), and higher concentrationsof kidney injury molecule-1 and midkine.

Baseline clinical variables of subjects in the validation set (N=278)were similar to those in the training set, and are included in Table 5(for diagnostic protein biomarkers) and Table 6 (for clinicalvariables). Table 5 below shows protein biomarker concentrations andtheir diagnostic association that differ between those in the validationset (N=278) with at least one coronary artery stenosis ≥70% (N=178) andthose who did not in the cohort of subjects who received a coronarycath, with or without an optional peripheral cath.

TABLE 5 Diagnostic Biomarkers (Received Coronary Cath; Peripheral CathOptional) (Validation Set) Concentration in Concentration in Subjectswith Subjects without Coronary Stenosis Coronary Stenosis Biomarker (N =178) (N = 100) p-value Adiponectin (ug/mL) 3.85 (2.225, 5.825) 4.3 (2.9,7.35) 0.03 Alpha-1-Antitrypsin (AAT) 1.8 (1.5, 2.175) 1.8 (1.5, 2.1)0.63 (mg/mL) Alpha-2-Macroglobulin 1.8 (1.5, 2.3) 1.8 (1.5, 2.2) 0.602(A2Macro) (mg/mL) Angiopoietin-1 (ANG-1) 6.4 (4.9, 9.6) 7.2 (5.1, 11)0.171 (ng/mL) Angiotensin-Converting 81 (62.2, 106) 77 (63.5, 104.8)0.934 Enzyme (ACE) (ng/mL) Apolipoprotein(a) (Lp(a)) 210 (73, 600.2)250.5 (68, 544) 0.929 (ug/mL) Apolipoprotein A-I (Apo A-I) 1.7 (1.4,2.1) 1.9 (1.6, 2.325) 0.007 (mg/mL) Apolipoprotein A-II (Apo A-II) 306.5(251.2, 383.8) 345 (276, 395.8) 0.058 (ng/mL) Apolipoprotein B (Apo B)1395 (1082, 1830) 1495 (1210, 1818) 0.116 (ug/mL) Apolipoprotein C-I(Apo C-I) (ng/mL) 304 (244.5, 362.5) 357.5 (303.5, 411) <0.001Apolipoprotein C-III (Apo C-III) 208 (158.2, 271.8) 215.5 (171.5, 256.2)0.844 (ug/mL) Apolipoprotein H (Apo H) 336.5 (278.2, 402) 329.5 (271.2,391.5) 0.457 (ug/mL) Beta-2-Microglobulin (B2M) 1.8 (1.4, 2.5) 1.75(1.3, 2.3) 0.355 (ug/mL) Brain-Derived Neurotrophic 2.2 (1.1, 4.275)2.65 (1.35, 5.7) 0.181 Factor (BDNF) (ng/mL) C-Reactive Protein (CRP) 4(1.4, 11) 4.55 (1.575, 10.25) 0.845 (ug/mL) Carbonic anhydrase 9 (CA-9)0.14 (0.089, 0.25) 0.12 (0.072, 0.2) 0.084 (ng/mL) Carcinoembryonicantigen- 24 (20, 29) 23.5 (20, 29) 0.924 related cell adhesion molecule1 (CEACAM1) (ng/mL) CD5 Antigen-like (CD5L) 3715 (2880, 4905) 3960(2782, 5170) 0.69 (ng/mL) Decorin (ng/mL) 2.4 (1.925, 3.4) 2.3 (1.9,3.2) 0.339 E-Selectin (ng/mL) 5.2 (3.6, 6.7) 5.8 (4.5, 7.1) 0.066EN-RAGE (ng/mL) 34 (19, 61.5) 23.5 (15.8, 48.2) 0.006 Eotaxin-1 (pg/mL)98 (42.5, 156) 97 (42.5, 131) 0.273 Factor VII (ng/mL) 455.5 (351.8,581.2) 491.5 (369.2, 605.8) 0.387 Ferritin (FRTN) (ng/mL) 145.5 (70.2,266.5) 135.5 (67.8, 223.2) 0.625 Fetuin-A (ug/mL) 677 (550.5, 826.2) 732(581.2, 825.8) 0.098 Fibrinogen (mg/mL) 4.5 (3.7, 5.575) 4.45 (3.5, 5.5)0.443 Follicle-Stimulating Hormone 6.2 (1.8, 13) 13.5 (5.8, 40.2) <0.001(FSH) (mIU/mL) Growth Hormone (GH) 0.38 (0.162, 1.3) 0.32 (0.13, 0.935)0.244 (ng/mL) Haptoglobin (mg/mL) 1.1 (0.565, 1.9) 1 (0.372, 1.9) 0.423Immunoglobulin A (IgA) 2.3 (1.625, 3.175) 2.3 (1.575, 3.325) 0.839(mg/mL) Immunoglobulin M (IgM) 1.3 (0.872, 1.975) 1.4 (0.988, 2.025)0.188 (mg/mL) Insulin (uIU/mL) 0.86 (0.11, 2.4) 0.495 (0.11, 1.35) 0.007Intercellular Adhesion 100 (86.2, 126) 107.5 (85, 134.2) 0.426 Molecule1 (ICAM-1) (ng/mL) Interferon gamma Induced 291 (221.5, 403) 304.5(225.2, 421.2) 0.691 Protein 10 (IP-10) (pg/mL) Interleukin-1 receptor121 (88, 157.8) 106.5 (79.5, 134.8) 0.046 antagonist (IL-1ra) (pg/mL)Interleukin-6 receptor (IL-6r) 25 (20, 30) 23 (18, 29) 0.142 (ng/mL)Interleukin-8 (IL-8) (pg/mL) 6.8 (4.5, 9.3) 6.2 (4.4, 9.1) 0.21Interleukin-12 Subunit p40 0.58 (0.49, 0.72) 0.575 (0.448, 0.702) 0.272(IL-12p40) (ng/mL) Interleukin-15 (IL-15) (ng/mL) 0.595 (0.46, 0.69)0.555 (0.45, 0.67) 0.271 Interleukin-18 (IL-18) (pg/mL) 213 (156.5,289.8) 180.5 (135.8, 234) 0.003 Interleukin-18-binding protein 9.9 (7.7,14) 8.7 (6.7, 12) 0.027 (IL-18bp) (ng/mL) Interleukin-23 (IL-23) (ng/mL)2.7 (2, 3.2) 2.5 (1.875, 3.2) 0.116 Kidney Injury Molecule-1 0.042(0.014, 0.084) 0.031 (0.014, 0.052) 0.005 (KIM-1) (ng/mL) Leptin (ng/mL)8 (4.2, 16.8) 10 (4.7, 21.2) 0.278 Luteinizing Hormone (LH) 4.7 (3, 7.8)7 (3.9, 13) <0.001 (mIU/mL) Macrophage Colony- 0.45 (0.16, 0.72) 0.39(0.16, 0.62) 0.051 Stimulating Factor 1 (M-CSF) (ng/mL) MacrophageInflammatory 273 (204.8, 366.8) 259 (208, 345.5) 0.693 Protein-1 beta(MIP-1 beta) (pg/mL) Matrix Metalloproteinase-2 1330 (1070, 1618) 1275(1050, 1602) 0.938 (MMP-2) (ng/mL) Matrix Metalloproteinase-3 7.2 (5.3,11) 5.6 (3.9, 7.8) <0.001 (MMP-3) (ng/mL) Matrix Metalloproteinase-70.345 (0.23, 0.55) 0.315 (0.22, 0.5) 0.255 (MMP-7) (ng/mL) MatrixMetalloproteinase-9 133.5 (97, 192) 111.5 (81.8, 169.2) 0.028 (MMP-9)(ng/mL) Matrix Metalloproteinase-9, 641.5 (463.5, 879.5) 526.5 (395,799.2) 0.022 total (MMP-9, total) (ng/mL) Midkine (ng/mL) 15 (10, 22.8)13 (9.3, 19) 0.066 Monocyte Chemotactic 104.5 (77, 152) 113.5 (73,152.8) 0.666 Protein 1 (MCP-1) (pg/mL) Monocyte Chemotactic 23.5 (17.2,30.8) 25 (19, 30.2) 0.339 Protein 2 (MCP-2) (pg/mL) Monocyte Chemotactic2140 (1530, 3200) 2415 (1575, 3262) 0.517 Protein 4 (MCP-4) (pg/mL)Monokine Induced by Gamma 1000 (575, 1748) 868 (536.8, 1462) 0.342Interferon (MIG) (pg/mL) Myeloid Progenitor Inhibitory 1.3 (1, 1.7) 1.2(0.992, 1.6) 0.553 Factor 1 (MPIF-1) (ng/mL) Myoglobin (ng/mL) 35 (24,47) 27 (21, 42.2) 0.007 N-terminal prohormone of 1415 (531, 4288) 1815(604.8, 4655) 0.424 brain natriuretic peptide (NT proBNP) (pg/mL)Osteopontin (ng/mL) 31 (21, 47.8) 25.5 (19.8, 38.2) 0.026 PancreaticPolypeptide (PPP) 86 (54, 161) 79.5 (44.8, 152.2) 0.145 (pg/mL)Plasminogen Activator 45.5 (28, 67.8) 44.5 (27, 77.2) 0.613 Inhibitor 1(PAI-1) (ng/mL) Platelet endothelial cell 53 (45.2, 65.8) 58 (47.5, 69)0.096 adhesion molecule (PECAM-1) (ng/mL) Prolactin (PRL) (ng/mL) 7(4.9, 11.8) 9 (5.7, 13) 0.042 Pulmonary and Activation- 102.5 (76.5,134) 96 (72.5, 142.2) 0.368 Regulated Chemokine (PARC) (ng/mL) Pulmonarysurfactant- 5.5 (3.6, 8.7) 5.3 (3.2, 8.4) 0.54 associated protein D(SP-D) (ng/mL) Resistin (ng/mL) 2.7 (2, 3.875) 2.3 (1.675, 3.3) 0.002Serotransferrin (Transferrin) 269 (224.5, 303.8) 264.5 (239.2, 319.5)0.859 (mg/dl) Serum Amyloid P-Component 13 (10, 17) 13 (9.5, 17) 0.472(SAP) (ug/mL) Stem Cell Factor (SCF) (pg/mL) 383 (293, 493.8) 345.5(290.8, 427.2) 0.025 T-Cell-Specific Protein RANTES 7.8 (4.1, 13.8) 11(4.1, 20.2) 0.121 (RANTES) (ng/mL) Tamm-Horsfall Urinary 0.03 (0.021,0.041) 0.032 (0.022, 0.041) 0.382 Glycoprotein (THP) (ug/mL)Thrombomodulin (TM) 4 (3.3, 4.975) 3.6 (3, 4.425) 0.015 (ng/mL)Thrombospondin-1 (ng/mL) 4415 (2248, 7315) 4610 (2285, 7835) 0.599Thyroid-Stimulating Hormone 1.2 (0.782, 1.875) 1.2 (0.728, 1.9) 0.931(TSH) (uIU/mL) Thyroxine-Binding Globulin 37 (31, 43) 38 (32, 45.2)0.246 (TBG) (ug/mL) Tissue Inhibitor of 72 (62, 92) 76 (58, 93.5) 0.921Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin (TTR) (mg/dl) 26(21, 31) 26 (21, 30.2) 0.962 Troponin (pg/ml) 10.3 (4, 110.6) 6.2 (3.1,20.8) 0.002 Tumor necrosis factor 6.7 (5, 9.7) 6.3 (4.5, 8.7) 0.097receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 581.5 (460, 752.8) 533(416.8, 693.5) 0.081 Molecule-1 (VCAM-1) (ng/mL) Vascular EndothelialGrowth 99.5 (71.2, 135) 93 (63, 145.5) 0.618 Factor (VEGF) (pg/mL)Vitamin D-Binding Protein 246.5 (190.5, 315.5) 250.5 (158.8, 314.2)0.902 (VDBP) (ug/mL) Vitamin K-Dependent Protein 14 (11, 17) 14 (11, 17)0.559 S (VKDPS) (ug/mL) Vitronectin (ug/mL) 445.5 (350, 560) 463.5(358.8, 581) 0.584 von Willebrand Factor (vWF) 139.5 (100, 188.8) 123(87, 166.8) 0.038 (ug/mL)

Table 6 below shows baseline clinical variables and their diagnosticassociation that differ between those in the validation set (N=278) withat least one coronary artery stenosis ≥70% (N=178) and those who did notin the cohort of subjects who received a coronary cath, with or withoutan optional peripheral cath.

TABLE 6 Diagnostic Clinical Variables (Received Coronary Cath;Peripheral Cath Optional) (Validation Set) Subjects with Subjects w/oCoronary Stenosis ≥70% Coronary Stenosis ≥70% Clinical Characteristics(N = 178) (N = 100) p-value Demographics Age (years) 67.8 (11.6) 65.5(11.8) 0.109 Male sex 144/178 (80.9%) 55/100 (55%) <0.001 Caucasian167/178 (93.8%) 95/100 (95%) 0.793 Vital Signs Heart rate (beat/min)67.6 (14.1) 70.8 (13.4) 0.071 Systolic BP (mmHg) 136.4 (22.2) 132.6(22.2) 0.182 Diastolic BP (mmHg) 73 (11.3) 70.5 (11.5) 0.099 MedicalHistory Smoking 29/176 (16.5%) 12/99 (12.1%) 0.381 Atrialfibrillation/flutter 30/178 (16.9%) 28/100 (28%) 0.032 Hypertension130/178 (73%) 70/100 (70%) 0.677 Coronary artery disease 106/178 (59.6%)29/100 (29%) <0.001 Myocardial infarction 47/178 (26.4%) 15/100 (15%)0.035 Heart failure 34/178 (19.1%) 21/100 (21%) 0.754 Peripheral arterydisease 38/178 (21.3%) 9/100 (9%) 0.008 COPD 25/178 (14%) 18/100 (18%)0.392 Diabetes, Type 1 1/178 (0.6%) 2/100 (2%) 0.294 Diabetes, Type 254/178 (30.3%) 14/100 (14%) 0.002 Any Diabetes 54/178 (30.3%) 16/100(16%) 0.009 CVA/TIA 24/178 (13.5%) 5/100 (5%) 0.026 Chronic kidneydisease 27/178 (15.2%) 7/100 (7%) 0.056 Hemodialysis 3/178 (1.7%) 3/100(3%) 0.67 Angioplasty, peripheral 19/178 (10.7%) 4/100 (4%) 0.068 and/orcoronary Stent, peripheral and/or 51/178 (28.7%) 14/100 (14%) 0.005coronary CABG 46/178 (25.8%) 3/100 (3%) <0.001 Percutaneous coronary87/178 (48.9%) 3/100 (3%) <0.001 intervention Medications ACE-I/ARB94/177 (53.1%) 51/99 (51.5%) 0.803 Beta blocker 120/177 (67.8%) 64/100(64%) 0.596 Aldosterone antagonist 8/177 (4.5%) 5/100 (5%) 1 Loopdiuretics 35/177 (19.8%) 22/100 (22%) 0.757 Nitrates 40/176 (22.7%)14/100 (14%) 0.085 CCB 48/178 (27%) 25/100 (25%) 0.777 Statin 136/177(76.8%) 62/100 (62%) 0.012 Aspirin 149/178 (83.7%) 59/100 (59%) <0.001Warfarin 26/177 (14.7%) 24/100 (24%) 0.073 Clopidogrel 55/177 (31.1%)10/100 (10%) <0.001 Echocardiographic results LVEF (%) 55.8 (15) 55.5(15.8) 0.904 RSVP (mmHg) 41.7 (11.6) 42 (12.7) 0.905 Stress test resultsIschemia on Scan 38/49 (77.6%) 10/12 (83.3%) 1 Ischemia on ECG 19/36(52.8%) 6/11 (54.5%) 1 Angiography results >=70% coronary stenosis104/178 (58.4%) 0/100 (0%) <0.001 in >=2 vessels >=70% coronary stenosis57/178 (32%) 0/100 (0%) <0.001 in >=3 vessels Lab Measures Sodium 139(3.5) 139.7 (3.1) 0.116 Blood urea nitrogen 19 (15, 25.2) 17 (14, 22.2)0.048 (mg/dL) Creatinine (mg/dL) 1.1 (0.9, 1.3) 1.1 (0.9, 1.2) 0.077eGFR (median, CKDEPI) 96.8 (73.5, 110.4) 98.5 (80.8, 110.5) 0.788 Totalcholesterol (mg/dL) 154.6 (45.8) 163.9 (41.9) 0.175 LDL cholesterol(mg/dL) 89.2 (40.2) 90 (29.4) 0.889 Glycohemoglobin (%) 6.2 (5.7, 6.6)6.2 (5.9, 6.9) 0.412 Glucose (mg/dL) 102 (95, 124) 104 (89.8, 123.2)0.936 HGB (mg/dL) 13.2 (1.7) 13.2 (1.5) 0.974

Following the described methods, from the training cohort (N=649),independent predictors of CAD ≥70% in any one vessel included fourbiomarkers (adiponectin, apolipoprotein C-I, kidney injury molecule-1,and midkine) and clinical variables (history of percutaneous coronaryintervention e.g., balloon angioplasty with or without stent placement,and sex). This combination of protein biomarkers and clinical variablesis represented by panel FM139/685 as shown in Table 25 and FIGS. 1-4.

Model fitting is displayed in Table 7, which shows that the addition ofeach individual biomarker to clinical variables improved discrimination,while simultaneously improving calibration for coronary stenosis of≥70%, as evidenced by minimization of the AIC or BIC, and withconcomitant goodness of fit through Hosmer-Lemeshow testing. Withrespect to the biomarkers, candidates were retained if they strengthenedthe model and/or improved calibration.

TABLE 7 Model Fitting for Diagnostic CAD Panel FM139/685, Example 1(Received Coronary Cath; Peripheral Cath Optional) (Validation Set)Hosmer- Lemeshow Model AUC AIC BIC p-value Model 1: Clinical factors0.79 671.7 685.1 1.0 alone Model 2: Clinical factors + 0.84 639.4 657.30.35 midkine Model 3: Clinical factors + 0.80 667.9 685.8 0.83adiponectin Model 4: Clinical factors + 0.84 662.4 680.3 0.99apolipoprotein C-I Model 5: Clinical factors + 0.83 648.2 666.1 0.77kidney injury molecule-1 Model 6: Clinical factors + 0.87 612.1 643.40.40 midkine, adiponectin, apolipoprotein C-I, and kidney injurymolecule-1 AUC = area under the curve, AIC = Akaike informationcriterion, BIC = Bayesian information criterion

In multivariable logistic regression, among those in the trainingcohort, our score was strongly predictive of severe CAD in all subjects(OR=9.74, 95% CI 6.05-16.1; P<0.001). To better understand performanceof the score in various subgroups, we then examined score performance inmen (OR=7.88, 95% CI=4.31-14.9; P<0.001), women (OR=24.8, 95%CI=7.11-111.6; P<0.001), as well as those without prior history of CAD(OR=8.67, 95% CI=4.38-17.9; P<0.001).

For the validation cohort, we calculated individual scores and expressedresults as a function of CAD presence. In doing so, a bimodal scoredistribution was revealed (FIG. 2), with higher prevalence of severe CADin those with higher scores, and lower prevalence among those with lowerscores. In ROC testing, for the gold standard diagnosis of ≥70% stenosisof any major epicardial coronary artery, the scores generated had an AUCof 0.87 (FIG. 1; P<0.001).

Table 8 shows the operating characteristics of the FM139/685 CADalgorithm across various scores. For sensitivity and specificity, the95% confidence interval is listed in parentheses. At the optimal scorecut-point, we found 77% sensitivity, 84% specificity, PPV of 90% and NPVof 67% for severe CAD. In subjects with a history of CAD the score had asensitivity of 84%, specificity of 66%, PPV of 90%, and NPV of 53% forprediction of CAD. In subjects without a history of CAD the score had asensitivity of 78%, specificity of 80%, PPV of 80%, and NPV of 78% forprediction of CAD. The CAD score was also tested for performance inpatients presenting with and without an MI. The AUC of the score forpredicting severe CAD in subjects presenting without an acute MI was0.87 (P<0.001).

TABLE 8 Performance of Diagnostic Panel FM139/685, Example 1 (ReceivedCoronary Cath; Peripheral Cath Optional) (Validation Set) CutoffSensitivity Specificity PPV NPV 6.5 0 (0, 0) 1 (1, 1) — 0.36 6 0.006 (0,0.017) 1 (1, 1) 1 0.361 5.5 0.011 (0, 0.027) 1 (1, 1) 1 0.362 5 0.034(0.007, 0.06) 1 (1, 1) 1 0.368 4.5 0.034 (0.007, 0.06) 1 (1, 1) 1 0.3684 0.118 (0.071, 0.165) 1 (1, 1) 1 0.389 3.5 0.281 (0.215, 0.347) 1(1, 1) 1 0.439 3 0.36 (0.289, 0.43) 0.97 (0.937, 1) 0.955 0.46 2.5 0.433(0.36, 0.505) 0.96 (0.922, 0.998) 0.951 0.487 2 0.506 (0.432, 0.579)0.95 (0.907, 0.993) 0.947 0.519 1.5 0.584 (0.512, 0.657) 0.91 (0.854,0.966) 0.92 0.552 1 0.674 (0.605, 0.743) 0.87 (0.804, 0.936) 0.902 0.60.5 0.787 (0.726, 0.847) 0.79 (0.71, 0.87) 0.87 0.675 0 0.904 (0.861,0.948) 0.61 (0.514, 0.706) 0.805 0.782 −0.5 0.961 (0.932, 0.989) 0.41(0.314, 0.506) 0.743 0.854 −1 0.989 (0.973, 1) 0.21 (0.13, 0.29) 0.690.913 −1.5 0.994 (0.983, 1) 0.09 (0.034, 0.146) 0.66 0.9 −2 1 (1, 1) 0(0, 0) 0.64 —

Table 9 below shows a scoring model using a five-level scoring system,and illustrates the performance of the model when the raw diagnosticvalue is partitioned into a five-level score, each optimized fordifferent operating characteristics and diagnostic confidence, with ahigher score indicating an increased risk for the presence of CAD.Scores 1-2 indicate a negative diagnosis, scores 4-5 indicates apositive diagnosis, and a score of 3 indicates a diagnosis of moderaterisk. The cutoffs for score levels 1 and 2 were optimized for NPV (0.9and 0.8 respectively), and tested in the validation set with NPV valuesof 0.91 and 0.76 respectively. The cutoffs for score levels 4 and 5 wereoptimized for PPV (0.9 and 0.95 respectively), and tested in thevalidation set with PPV values of 0.85 and 0.93 respectively. This isalso depicted in FIG. 3.

TABLE 9 Performance of 5-Level Score for Diagnostic Panel FM139/685,Example 1 (Received Coronary Cath; Peripheral Cath Optional) (ValidationSet) Optimized For Observed in Validation Set Score # Patients PPV NPVPPV NPV 5 107 0.95 — 0.925 — 4 39 0.9  — 0.846 — 3 78 NA NA 0.462 0.5382 43 — 0.8 — 0.791 1 11 — 0.9 — 0.909

Notably, among those with available data to calculate the FraminghamRisk Score (N=577), we found the CAD algorithm to have consistent andsuperior AUC over the Framingham Risk Score's ability to predict thepresence of CAD (0.87 versus 0.52; P<0.001). Of the 649 in the trainingset, 154 had exercise stress tests without imaging and 174 had nuclearstress tests; of the 278 patients in the validation set, 47 had exercisestress tests without imaging and 61 had nuclear stress tests. Amongpatients undergoing cardiac stress testing per standard of care, the CADscore was substantially more accurate for predicting angiographicallysevere CAD (again, 0.87 vs. 0.52; P<0.001 for difference in AUC).

During a mean follow up of 3.6 years, in the entire cohort of subjects,the CAD diagnostic scoring system independently predicted subsequentincident of acute MI in age- and score-adjusted models (HR=2.39; 95%CI=1.65-3.47; P<0.001). When modeled as a continuous variable, the scorewas similarly predictive, with higher scores predictive of higher riskfor incident acute MI (HR=1.19 per unit score increase; 95%CI=1.09-1.31; P<0.001). Those with a dichotomously elevated score had ashorter time to first event than those with a lower CAD score, asevidenced by rapid and sustained divergence of the Kaplan-Meier survivalcurves (FIG. 4; log rank p-value <0.001).

Using patients referred for coronary angiography for a broad range ofindications, we describe a novel scoring system to predict the presenceof severe epicardial CAD (≥70% stenosis in at least one major vessel).This score consisted of a combination of clinical variables andconcentrations of 4 biologically relevant biomarkers. For the diagnosisof ≥70% stenosis of any major epicardial coronary artery, the scoregenerated had an area under the ROC curve of 0.87 in the validation set,and at the optimal cut-point, the score was both highly sensitive (77%)and specific (84%) for the diagnosis of CAD, with a PPV of 90%.Importantly, the CAD score performed particularly well in women, andwhile one element of the score was history of percutaneous coronaryintervention (also referred to as PCI), the score performance wassimilar in subjects without a history of CAD or in those without an MIat presentation. Among those with available data to calculate aFramingham Risk Score, the CAD score had significantly higher AUC whenpredicting the presence of CAD than did the Framingham Risk Score; suchdata are similar to Pen and colleagues, who found the Framingham scoreto be less accurate for determining prevalent coronary plaque burdendetected with CT angiography. The score also performed significantlybetter for the diagnosis of CAD than stress testing.

The clinical and biomarker scoring strategy disclosed herein canreliably diagnose the presence of severe epicardial CAD. Advantages of areliable clinical and biomarker score for diagnosing CAD presenceinclude the fact such a technology can be widely disseminated in acost-effective manner, easily interpreted, and are associated with awell-defined sequence of therapeutic steps.

Example 2: A Clinical and Biomarker Scoring System to DiagnoseObstructive Coronary Artery Disease (CAD), Panel FM46/572

This example demonstrates yet another non-invasive method employing aclinical and biomarker scoring system that offers, among other things,high accuracy in diagnosing the presence of anatomically significantCAD, and in providing a prognosis of cardiovascular events. This exampleutilized the same described methods (study design and participants, dataacquisition, follow up, biomarker testing, statistics and results(Tables 1A, 3A, 5, and 6; FIGS. 18-20) as Example 1. The primarydifferences between Example 1 and Example 2 are the clinical variablesand proteins that were utilized.

Following the described methods, from the training cohort (N=649),independent predictors of CAD ≥70% in any one vessel included threebiomarkers (adiponectin, decorin, and midkine) and three clinicalvariables (history of myocardial infarct, history of percutaneouscoronary intervention and sex). This combination of biomarkers andclinical variables are represented in panel FM46/572 as shown in Table25 and FIGS. 18-20.

Model fitting is displayed in Table 10, which shows that the addition ofeach individual biomarker to clinical variables improved discrimination,while simultaneously improving calibration for coronary stenosis of≥70%, as evidenced by minimization of the AIC or BIC, and withconcomitant goodness of fit through Hosmer-Lemeshow testing. Withrespect to the biomarkers, candidates were retained if they strengthenedthe model and/or improved calibration.

TABLE 10 Model Fitting for Diagnostic CAD Panel FM46/572, Example 2(Received Coronary Cath; Peripheral Cath Optional) (Validation Set)Hosmer- Lemeshow Model AUC AIC BIC p-value Model 1: Clinical factorsalone 0.80 639.3 657.2 0.999 Model 2: Clinical factors + 0.84 610.7633.1 0.806 midkine Model 3: Clinical factors + 0.81 637.8 660.2 0.956adiponectin Model 4: Clinical factors + 0.80 636.9 659.2 0.564 decorinModel 5: Clinical factors + 0.84 600.0 626.8 0.386 adiponectin, midkineModel 6: Clinical factors + 0.84 595.1 626.4 0.987 adiponectin, decorin,midkine

Notably, those with severe CAD had lower concentrations of adiponectinand higher concentrations of midkine and decorin. Biomarkers and theirconcentrations in subjects in the training set with and without coronarystenosis are shown in Table 1A, and the baseline clinical variablevalues in the training set are found in Table 3A. Baseline biomarker andclinical variables of subjects in the validation set (N=278) areincluded in Tables 5 and 6, respectively.

For the validation cohort, in ROC testing, for the gold standarddiagnosis of ≥70% stenosis of any major epicardial coronary artery, thescores generated had an AUC of 0.84 (FIG. 18; P<0.001). For thevalidation cohort, individual scores were calculated and expressedresults as a function of CAD presence. In doing so, a bimodal scoredistribution was revealed (FIG. 19), with higher prevalence of severeCAD in those with higher scores, and lower prevalence among those withlower scores.

Table 11 below shows the operating characteristics of the FM46/572 CADalgorithm across various scores. For sensitivity and specificity, the95% confidence interval is listed in parentheses. At the optimal scorecut-point, we found 69.7% sensitivity, 81.0% specificity, PPV of 86.7%and NPV of 60.0% for severe CAD.

TABLE 11 Performance of Diagnostic Panel FM46/572, Example 2 (ReceivedCoronary Cath; Peripheral Cath Optional) (Validation Set) CutoffSensitivity Specificity PPV NPV 6.5 0 (0, 0) 1 (1, 1) NA 0.36 6 0.006(−0.005, 0.017) 1 (1, 1) 1 0.361 5.5 0.006 (−0.005, 0.017) 1 (1, 1) 10.361 5 0.017 (−0.002, 0.036) 1 (1, 1) 1 0.364 4.5 0.062 (0.026, 0.097)1 (1, 1) 1 0.375 4 0.146 (0.094, 0.198) 0.98 (0.953, 1.007) 0.929 0.3923.5 0.236 (0.174, 0.298) 0.98 (0.953, 1.007) 0.955 0.419 3 0.354 (0.284,0.424) 0.97 (0.937, 1.003) 0.955 0.458 2.5 0.466 (0.393, 0.54) 0.96(0.922, 0.998) 0.954 0.503 2 0.551 (0.477, 0.624) 0.95 (0.907, 0.993)0.951 0.543 1.5 0.59 (0.518, 0.662) 0.9 (0.841, 0.959) 0.913 0.552 10.657 (0.588, 0.727) 0.87 (0.804, 0.936) 0.9 0.588 0.5 0.764 (0.702,0.826) 0.77 (0.688, 0.852) 0.855 0.647 0 0.848 (0.796, 0.901) 0.63(0.535, 0.725) 0.803 0.7 −0.5 0.916 (0.875, 0.957) 0.38 (0.285, 0.475)0.724 0.717 −1 0.966 (0.94, 0.993) 0.22 (0.139, 0.301) 0.688 0.786 −1.50.994 (0.983, 1.005) 0.05 (0.007, 0.093) 0.651 0.833 −2 1 (1, 1) 0.01(−0.01, 0.03) 0.643 1 −2.5 1 (1, 1) 0 (0, 0) 0.64 NA

Table 12 below shows the scoring model using a three-level scoringsystem, and illustrates the performance of the model when the rawdiagnostic value is partitioned into a three-level score, each optimizedfor different operating characteristics and diagnostic confidence, witha higher score indicating an increased risk for the presence of CAD. Ina three-level score, a score of 1 indicates a negative or low likelihoodof CAD diagnosis, a score of 3 indicates a positive or high likelihoodof CAD diagnosis, and a score of two indicates a diagnosis of moderatelikelihood of CAD diagnosis. The cutoff for score level 1 was optimizedfor NPV of 0.8 in the training set, and the cutoff for score level of 3was optimized for a PPV of 0.95 in the training set.

TABLE 12 Performance of 3-Level Score for Diagnostic Panel FM46/572,Example 2 (Received Coronary Cath; Peripheral Cath Optional) (ValidationSet) Optimized For Observed in Validation Set Score # Patients PPV NPVPPV NPV 3 103 0.95 — 0.951 — 2 85 NA NA 0.624 0.376 1 90 — 0.8 — 0.700

Notably, among those with available data to calculate the FraminghamRisk Score (N=577), we found the CAD algorithm to have consistent andsuperior AUC over the Framingham Risk Score's ability to predict thepresence of CAD (0.84 versus 0.52; P<0.001). Of the 649 in the trainingset, 154 had exercise stress tests without imaging and 174 had nuclearstress tests; of the 278 patients in the validation set, 47 had exercisestress tests without imaging and 61 had nuclear stress tests. Amongpatients undergoing cardiac stress testing per standard of care, the CADscore was substantially more accurate for predicting angiographicallysevere CAD (again, 0.84 vs. 0.52; P<0.001 for difference in AUC).

During a mean follow up of 3.6 years, in the entire cohort of subjects,the CAD scoring system independently predicted subsequent incident acuteMI in age- and score-adjusted models (HR=1.85; 95% CI=1.28-2.67;P<0.001). When modeled as a continuous variable, the score was similarlypredictive, with higher scores predictive of higher risk for incidentacute MI (HR=1.19 per unit score increase; 95% CI=1.08-1.30; P<0.001).Those with a dichotomously elevated score had a shorter time to firstevent than those with a lower CAD score, as evidenced by rapid andsustained divergence of the Kaplan-Meier survival curves (FIG. 20; logrank p-value <0.001).

Example 3: A Clinical and Biomarker Scoring System to DiagnoseObstructive Coronary Artery Disease (CAD), Panel FM02/410

This example demonstrates yet another non-invasive method employing aclinical and biomarker scoring system that offers, among other things,high accuracy in diagnosing the presence of anatomically significantCAD, and in providing a prognosis of cardiovascular events.

This example utilized the same described methods as Example 1 and 2(study design, data acquisition, follow up, biomarker testing,statistics and results), with the exception of the subjects. Thisexample included those subjects that only had a coronarycatheterization, N=809, (vs. subjects with a coronary catheterizationand optionally a peripheral catheterization (Tables 1B, 3B, 13, 14, 15and 16 and FIG. 28).

The patients selected for analysis consisted of the chronologicallyinitial 809 patients who received only a coronary angiogram. Patientswho may have also received a peripheral angiogram concomitantly wereexcluded.

The 809 patients selected for analysis were randomly split into atraining set (70%, or N=566, Tables 1B and 3B) and a holdout validationset (30%, or N=243, Tables 13 and 14). Baseline protein and clinicalcharacteristics between those with and without ≥70% coronary stenosis inat least one major epicardial coronary artery were compared; dichotomousvariables were compared using two-sided Fishers exact test, whilecontinuous variables were compared using two-sided two-sample T test.The biomarkers compared were tested with the Wilcoxon Rank Sum test, astheir concentrations were not normally distributed. For any markerresult that was unmeasurable, we utilized a standard approach ofimputing concentrations 50% below the limit of detection.

Table 13 below shows biomarker concentrations and their diagnosticassociation that differ between those in the validation set (N=243) withat least one coronary artery stenosis ≥70% (N=148) and those who did notin the cohort of subjects who received a coronary cath only.

TABLE 13 Diagnostic Biomarkers for Diagnostic Panel FM02/410, Example 3(Received Coronary Cath Only) (Validation Set) Concentration inConcentration in Subjects with Subjects without Coronary StenosisCoronary Stenosis Biomarker (N = 148) (N = 95) p-value Adiponectin(ug/mL) 3.85 (2.2, 5.925) 4.3 (2.85, 7.2) 0.067 Alpha-1-Antitrypsin(AAT) 1.8 (1.5, 2.225) 1.8 (1.5, 2.05) 0.439 (mg/mL)Alpha-2-Macroglobulin 1.8 (1.5, 2.2) 1.8 (1.5, 2.2) 0.893 (A2Macro)(mg/mL) Angiopoietin-1 (ANG-1) 6.4 (4.9, 9) 7.2 (5, 11) 0.18 (ng/mL)Angiotensin-Converting 79 (59.8, 105.2) 80 (64, 108) 0.55 Enzyme (ACE)(ng/mL) Apolipoprotein(a) (Lp(a)) 195 (71, 557.8) 238 (76.5, 549) 0.688(ug/mL) Apolipoprotein A-I (Apo A-I) 1.7 (1.4, 2.1) 1.9 (1.6, 2.35)0.008 (mg/mL) Apolipoprotein A-II (Apo A-II) 303.5 (250.8, 381.5) 344(277.5, 394) 0.045 (ng/mL) Apolipoprotein B (Apo B) 1395 (1080, 1830)1490 (1210, 1905) 0.082 (ug/mL) Apolipoprotein C-I (Apo C-I) 304 (243,363.8) 357 (298.5, 410) <0.001 (ng/mL) Apolipoprotein C-III (Apo C-III)208 (153.8, 274.8) 214 (171, 260.5) 0.823 (ug/mL) Apolipoprotein H (ApoH) 330 (273.8, 398.5) 331 (270.5, 388.5) 0.751 (ug/mL)Beta-2-Microglobulin (B2M) 1.8 (1.375, 2.325) 1.7 (1.3, 2.35) 0.672(ug/mL) Brain-Derived Neurotrophic 2.15 (1.1, 4.2) 2.6 (1.09, 5.4) 0.292Factor (BDNF) (ng/mL) C-Reactive Protein (CRP) 4.15 (1.55, 11.25) 4.4(1.55, 9.8) 0.85 (ug/mL) Carbonic anhydrase 9 (CA-9) 0.14 (0.089, 0.26)0.13 (0.074, 0.2) 0.117 (ng/mL) Carcinoembryonic antigen- 24 (20, 29) 23(20, 29) 0.926 related cell adhesion molecule 1 (CEACAM1) (ng/mL) CD5Antigen-like (CD5L) 3680 (2780, 4882) 4030 (2865, 5330) 0.288 (ng/mL)Decorin (ng/mL) 2.35 (1.975, 3.7) 2.3 (1.9, 3.2) 0.352 E-Selectin(ng/mL) 5.2 (3.7, 6.7) 5.8 (4.5, 7.1) 0.056 EN-RAGE (ng/mL) 31.5 (19,56.5) 23 (15.5, 48.5) 0.012 Eotaxin-1 (pg/mL) 103 (42.5, 160.5) 97(42.5, 130) 0.123 Factor VII (ng/mL) 458.5 (353.2, 578.2) 479 (367.5,601.5) 0.466 Ferritin (FRTN) (ng/mL) 147.5 (72.5, 258.5) 135 (72, 234)0.677 Fetuin-A (ug/mL) 662.5 (546.8, 822.8) 735 (588.5, 819) 0.07Fibrinogen (mg/mL) 4.5 (3.6, 5.525) 4.4 (3.5, 5.45) 0.434Follicle-Stimulating Hormone 6.4 (3.1, 13.2) 10 (5.7, 40.5) <0.001 (FSH)(mIU/mL) Growth Hormone (GH) 0.375 (0.16, 1.2) 0.31 (0.07, 0.94) 0.363(ng/mL) Haptoglobin (mg/mL) 1.2 (0.588, 1.9) 1 (0.34, 1.8) 0.173Immunoglobulin A (IgA) 2.3 (1.7, 3.2) 2.3 (1.65, 3.25) 0.995 (mg/mL)Immunoglobulin M (IgM) 1.35 (0.868, 2) 1.4 (0.985, 2.05) 0.21 (mg/mL)Insulin (uIU/mL) 0.725 (0.11, 2.3) 0.5 (0.11, 1.4) 0.037 IntercellularAdhesion 101 (82, 126) 108 (85, 134.5) 0.413 Molecule 1 (ICAM-1) (ng/mL)Interferon gamma Induced 295 (221, 432.5) 309 (230.5, 440.5) 0.828Protein 10 (IP-10) (pg/mL) Interleukin-1 receptor 122 (87.8, 160.2) 106(79, 135.5) 0.042 antagonist (IL-1ra) (pg/mL) Interleukin-6 receptor(IL-6r) 25 (19, 30) 23 (18, 29) 0.27 (ng/mL) Interleukin-8 (IL-8)(pg/mL) 6.7 (4.2, 9.3) 6.1 (4.3, 8.8) 0.239 Interleukin-12 Subunit p400.575 (0.49, 0.72) 0.57 (0.445, 0.715) 0.297 (IL-12p40) (ng/mL)Interleukin-15 (IL-15) (ng/mL) 0.585 (0.46, 0.69) 0.56 (0.45, 0.68)0.363 Interleukin-18 (IL-18) (pg/mL) 216 (155.5, 292.5) 181 (136, 234)0.004 Interleukin-18-binding protein 9.9 (7.3, 14) 8.7 (6.8, 12) 0.082(IL-18bp) (ng/mL) Interleukin-23 (IL-23) (ng/mL) 2.65 (2, 3.2) 2.5(1.85, 3.2) 0.099 Kidney Injury Molecule-1 0.04 (0.014, 0.08) 0.031(0.014, 0.053) 0.012 (KIM-1) (ng/mL) Leptin (ng/mL) 7.9 (4.2, 15) 10(4.7, 21.5) 0.162 Luteinizing Hormone (LH) 4.6 (3, 7.85) 6.8 (3.9, 13)0.002 (mIU/mL) Macrophage Colony- 0.435 (0.16, 0.672) 0.38 (0.16, 0.615)0.084 Stimulating Factor 1 (M-CSF) (ng/mL) Macrophage Inflammatory 271.5(201, 363.5) 262 (209.5, 350) 0.915 Protein-1 beta (MIP-1 beta) (pg/mL)Matrix Metalloproteinase-2 1325 (1068, 1605) 1270 (1050, 1620) 0.905(MMP-2) (ng/mL) Matrix Metalloproteinase-3 7 (5.1, 11) 5.7 (4, 7.8)0.002 (MMP-3) (ng/mL) Matrix Metalloproteinase-7 0.34 (0.23, 0.535) 0.3(0.22, 0.495) 0.31 (MMP-7) (ng/mL) Matrix Metalloproteinase-9 131.5 (93,192) 111 (81.5, 169.5) 0.068 (MMP-9) (ng/mL) Matrix Metalloproteinase-9,625 (450.2, 877.8) 526 (393, 806) 0.081 total (MMP-9, total) (ng/mL)Midkine (ng/mL) 15 (9.9, 21.2) 13 (9.3, 19) 0.225 Monocyte Chemotactic104.5 (77.8, 158) 115 (74.5, 151) 0.782 Protein 1 (MCP-1) (pg/mL)Monocyte Chemotactic 23.5 (18, 31) 25 (19.5, 30.5) 0.349 Protein 2(MCP-2) (pg/mL) Monocyte Chemotactic 2125 (1538, 3050) 2440 (1570, 3220)0.396 Protein 4 (MCP-4) (pg/mL) Monokine Induced by Gamma 1040 (559.5,1992) 962 (547, 1480) 0.484 Interferon (MIG) (pg/mL) Myeloid ProgenitorInhibitory 1.3 (1, 1.6) 1.2 (0.95, 1.6) 0.475 Factor 1 (MPIF-1) (ng/mL)Myoglobin (ng/mL) 34 (22.8, 45) 27 (21, 40.5) 0.038 N-terminalprohormone of 1290 (475.5, 3675) 1710 (575, 4750) 0.366 brainnatriuretic peptide (NT proBNP) (pg/mL) Osteopontin (ng/mL) 30 (20,46.2) 25 (18.5, 38) 0.046 Pancreatic Polypeptide (PPP) 86.5 (51.8,155.8) 80 (40.5, 158.5) 0.308 (pg/mL) Plasminogen Activator 45.5 (27.8,69.5) 44 (27, 76.5) 0.736 Inhibitor 1 (PAI-1) (ng/mL) Plateletendothelial cell 53 (45, 65.2) 58 (48, 68.5) 0.075 adhesion molecule(PECAM-1) (ng/mL) Prolactin (PRL) (ng/mL) 7 (4.7, 11.2) 8.9 (5.8, 13)0.033 Pulmonary and Activation- 98.5 (75.8, 134) 96 (72, 142.5) 0.47Regulated Chemokine (PARC) (ng/mL) Pulmonary surfactant- 5.1 (3.6, 8.2)5.3 (3.2, 8.4) 0.683 associated protein D (SP-D) (ng/mL) Resistin(ng/mL) 2.6 (1.9, 3.625) 2.3 (1.7, 3.3) 0.042 Serotransferrin(Transferrin) 270.5 (224, 301.5) 265 (240, 323.5) 0.704 (mg/dl) SerumAmyloid P-Component 13 (10, 17.2) 13 (9.4, 17) 0.299 (SAP) (ug/mL) StemCell Factor (SCF) (pg/mL) 384 (295, 496.5) 345 (287.5, 431.5) 0.037T-Cell-Specific Protein RANTES 7.8 (4, 14) 11 (4, 19.5) 0.249 (RANTES)(ng/mL) Tamm-Horsfall Urinary 0.03 (0.022, 0.042) 0.032 (0.021, 0.042)0.804 Glycoprotein (THP) (ug/mL) Thrombomodulin (TM) 4 (3.3, 4.825) 3.6(3, 4.4) 0.025 (ng/mL) Thrombospondin-1 (ng/mL) 4415 (2175, 7255) 4500(2225, 7615) 0.725 Thyroid-Stimulating Hormone 1.25 (0.822, 1.825) 1.3(0.735, 1.9) 0.963 (TSH) (uIU/mL) Thyroxine-Binding Globulin 37 (30.8,43) 38 (32, 46) 0.234 (TBG) (ug/mL) Tissue Inhibitor of 70 (59.8, 89) 76(58, 92.5) 0.766 Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin(TTR) (mg/dl) 25.5 (21, 31) 26 (22, 30.5) 0.691 Troponin (pg/ml) 10.2(3.8, 111.2) 6 (2.8, 20.8) 0.003 Tumor necrosis factor 6.7 (5, 9.5) 6.3(4.5, 9) 0.208 receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 570.5(459, 725.5) 536 (420.5, 697) 0.232 Molecule-1 (VCAM-1) (ng/mL) VascularEndothelial Growth 100 (73, 136.2) 93 (63, 147.5) 0.628 Factor (VEGF)(pg/mL) Vitamin D-Binding Protein 246.5 (189.2, 314.5) 250 (140.5,311.5) 0.881 (VDBP) (ug/mL) Vitamin K-Dependent Protein 13 (11, 17) 14(11, 17) 0.477 S (VKDPS) (ug/mL) Vitronectin (ug/mL) 445.5 (341, 556.2)465 (356.5, 584) 0.466 von Willebrand Factor (vWF) 138 (99, 189) 125(88, 166) 0.086 (ug/mL)

Table 14 below shows baseline clinical variables and their diagnosticassociation that differ between those in the validation set (N=243) withat least one coronary artery stenosis ≥70% (N=148) and those who did notin the cohort of subjects who received a coronary cath only.

TABLE 14 Diagnostic Clinical Variables for Diagnostic Panel FM02/410,Example 3 (Received Coronary Cath Only) (Validation Set) Subjects withSubjects w/o Coronary Stenosis ≥70% Coronary Stenosis ≥70% ClinicalCharacteristics (N = 148) (N = 95) p-value Demographics Age (years) 67.5(12.2) 65.4 (12) 0.176 Male sex 120/148 (81.1%) 53/95 (55.8%) <0.001Caucasian 140/148 (94.6%) 90/95 (94.7%) 1 Vital Signs Heart rate(beat/min) 68.2 (14.4) 70.4 (12.9) 0.212 Systolic BP (mmHg) 136.1 (22.1)133.6 (22.2) 0.411 Diastolic BP (mmHg) 73.7 (11.7) 71.3 (11.1) 0.121Medical History Smoking 25/146 (17.1%) 9/94 (9.6%) 0.129 Atrialfibrillation/flutter 27/148 (18.2%) 26/95 (27.4%) 0.112 Hypertension105/148 (70.9%) 68/95 (71.6%) 1 Coronary artery disease 88/148 (59.5%)27/95 (28.4%) <0.001 Myocardial infarction 37/148 (25%) 14/95 (14.7%)0.075 Heart failure 24/148 (16.2%) 19/95 (20%) 0.493 Peripheral arterydisease 22/148 (14.9%) 8/95 (8.4%) 0.164 COPD 21/148 (14.2%) 17/95(17.9%) 0.472 Diabetes, Type 1 0/148 (0%) 2/95 (2.1%) 0.152 Diabetes,Type 2 42/148 (28.4%) 13/95 (13.7%) 0.008 Any Diabetes 42/148 (28.4%)15/95 (15.8%) 0.03 CVA/TIA 20/148 (13.5%) 5/95 (5.3%) 0.05 Chronickidney disease 15/148 (10.1%) 7/95 (7.4%) 0.503 Hemodialysis 2/148(1.4%) 3/95 (3.2%) 0.382 Angioplasty, peripheral 12/148 (8.1%) 4/95(4.2%) 0.295 and/or coronary Stent, peripheral and/or 43/148 (29.1%)14/95 (14.7%) 0.013 coronary CABG 35/148 (23.6%) 3/95 (3.2%) <0.001Percutaneous coronary 79/148 (53.4%) 3/95 (3.2%) <0.001 interventionMedications ACE-I/ARB 77/147 (52.4%) 50/94 (53.2%) 1 Beta blocker 95/147(64.6%) 61/95 (64.2%) 1 Aldosterone antagonist 7/147 (4.8%) 5/95 (5.3%)1 Loop diuretics 28/147 (19%) 20/95 (21.1%) 0.743 Nitrates 27/146(18.5%) 13/95 (13.7%) 0.378 CCB 41/148 (27.7%) 24/95 (25.3%) 0.767Statin 113/147 (76.9%) 59/95 (62.1%) 0.02 Aspirin 123/148 (83.1%) 58/95(61.1%) <0.001 Warfarin 24/147 (16.3%) 23/95 (24.2%) 0.138 Clopidogrel45/147 (30.6%) 10/95 (10.5%) <0.001 Echocardiographic results LVEF (%)54.6 (15.1) 56.2 (15.7) 0.541 RSVP (mmHg) 41.2 (12.4) 41.7 (12.9) 0.856Stress test results Ischemia on Scan 28/38 (73.7%) 9/11 (81.8%) 0.708Ischemia on ECG 13/27 (48.1%) 6/11 (54.5%) 1 Angiography results >=70%coronary stenosis 84/148 (56.8%) 0/95 (0%) <0.001 in >=2 vessels >=70%coronary stenosis 43/148 (29.1%) 0/95 (0%) <0.001 in >=3 vessels LabMeasures Sodium 139.3 (3.2) 139.9 (3.1) 0.209 Blood urea nitrogen 18(15, 22.2) 18 (14.5, 23) 0.478 (mg/dL) Creatinine (mg/dL) 1.1 (0.9, 1.3)1.1 (0.9, 1.2) 0.371 eGFR (median, CKDEPI) 100.4 (75.6, 111.1) 98.5 (81,110.7) 0.791 Total cholesterol (mg/dL) 153.8 (45.9) 164.9 (43.2) 0.143LDL cholesterol (mg/dL) 87.8 (40.2) 90.2 (30.3) 0.692 Glycohemoglobin(%) 6 (5.7, 6.4) 6.2 (5.8, 7) 0.316 Glucose (mg/dL) 101 (95, 118.5) 103(89, 123.5) 0.986 HGB (mg/dL) 13.2 (1.7) 13.2 (1.5) 0.69

Additional differences in Example 3 (as compared for Example 1 and 2)are the clinical variables and proteins which were utilized in the panel(FM02/410). Following the described methods, from the training cohort(N=566), independent predictors of CAD ≥70% in any one vessel includedsix biomarkers (adiponectin, apolipoprotein C-1, matrixmetalloproteinase 9, midkine, myogloblin, and pulmonary surfactantprotein D) and three clinical variables (history of coronary arterybypass graft surgery [CABG], history of percutaneous coronaryintervention [e.g. balloon angioplasty with or without stent placement],and sex). This combination of biomarkers and clinical variables isrepresented by panel FM02/410 as shown in Table 25 and FIG. 28.

Table 15 below shows the operating characteristics of the FM02/410 CADalgorithm across various scores. For sensitivity and specificity, the95% confidence interval is listed in parentheses. At the optimal scorecut-point, we found 84.5% sensitivity, 77.9% specificity, PPV of 85.6%and NPV of 76.3% for severe CAD.

TABLE 15 Performance of Diagnostic Panel FM02/410, Example 3 (ReceivedCoronary Cath Only) (Validation Set) Cutoff Sensitivity Specificity PPVNPV 8.5 0 (0, 0) 1 (1, 1) — 0.391 8 0.007 (−0.006, 0.02) 1 (1, 1) 10.393 7.5 0.027 (0.001, 0.053) 1 (1, 1) 1 0.397 7 0.068 (0.027, 0.108)0.989 (0.969, 1.01) 0.909 0.405 6.5 0.095 (0.047, 0.142) 0.989 (0.969,1.01) 0.933 0.412 6 0.122 (0.069, 0.174) 0.989 (0.969, 1.01) 0.947 0.425.5 0.122 (0.069, 0.174) 0.979 (0.95, 1.008) 0.9 0.417 5 0.122 (0.069,0.174) 0.979 (0.95, 1.008) 0.9 0.417 4.5 0.149 (0.091, 0.206) 0.979(0.95, 1.008) 0.917 0.425 4 0.203 (0.138, 0.267) 0.979 (0.95, 1.008)0.938 0.441 2.5 0.324 (0.249, 0.4) 0.979 (0.95, 1.008) 0.96 0.482 30.453 (0.373, 0.533) 0.968 (0.933, 1.004) 0.957 0.532 2.5 0.541 (0.46,0.621) 0.947 (0.902, 0.992) 0.941 0.57 2 0.622 (0.543, 0.7) 0.947(0.902, 0.992) 0.948 0.616 1.5 0.649 (0.572, 0.726) 0.937 (0.888, 0.986)0.941 0.631 1 0.696 (0.622, 0.77) 0.926 (0.874, 0.979) 0.936 0.662 0.50.77 (0.702, 0.838) 0.842 (0.769, 0.915) 0.884 0.702 0 0.851 (0.794,0.909) 0.758 (0.672, 0.844) 0.846 0.766 −0.5 0.926 (0.883, 0.968) 0.537(0.437, 0.637) 0.757 0.823 −1 0.966 (0.937, 0.995) 0.326 (0.232, 0.421)0.691 0.861 −1.5 0.993 (0.98, 1.006) 0.147 (0.076, 0.219) 0.645 0.933 −21 (1, 1) 0.032 (−0.004, 0.067) 0.617 1 −2.5 1 (1, 1) 0 (0, 0) 0.609 —

Table 16 shows the scoring model using a three-level scoring system, andillustrates the performance of the model when the raw diagnostic valueis partitioned into a three-level score, each optimized for differentoperating characteristics and diagnostic confidence, with a higher scoreindicating an increased risk for the presence of CAD. In a three-levelscore, a score of 1 indicates a negative or low likelihood of CADdiagnosis, a score of 3 indicates a positive or high likelihood of CADdiagnosis, and a score of 2 indicates a diagnosis of moderate likelihoodof CAD diagnosis. The cutoff for score level 1 was optimized for NPV of0.8 in the training set, and the cutoff for score level of 3 wasoptimized for a PPV of 0.95 in the training set.

TABLE 16 Performance of 3-Level Score for Diagnostic Panel FM02/410(Received Coronary Cath Only) (Validation Set) Optimized For Observed inValidation Set Score # Patients PPV NPV PPV NPV 3 110 0.95 — 0.936 — 271 NA NA 0.479 0.521 1 62 — 0.8 — 0.823

Example 4: A Biomarker Scoring System to Predict 3 Day Post Sample Drawto One Year (3-365 Day) Risk of Composite Cardiovascular Death,Myocardial Infarct or Stroke (FM160/02)

This example demonstrates a non-invasive method employing a biomarkerscoring system that offers, among other things, high accuracy inproviding a prognosis of one year risk of composite cardiovasculardeath, myocardial infarct, or stroke. This example utilized the samedescribed methods as Example 1 (study design and subjects, dataacquisition, follow up, biomarker testing, statistics and results(Tables 2B, 4B, 17, 18, 19 and 20 and FIG. 30).

The patients selected for analysis consisted of the chronologicallyinitial 927 patients who received only a coronary angiogram. Patientsmay have also received a peripheral angiogram concomitantly wereexcluded.

The 927 patients selected for analysis were randomly split into atraining set (70%, or N=649; Tables 2B and 4B), and a holdout validationset (30%, or N=278; Tables 17 and 18). The training set had one subjectwho died on the day following the cath procedure (i.e., prior to Day 3),so this patient was removed from the training set, resulting in apopulation of N=648. Baseline clinical variables between those with andwithout MACE was compared (Tables 4B and 18); dichotomous variables werecompared using two-sided Fishers exact test, while continuous variableswere compared using two-sided two-sample T test. The biomarkers comparedwere tested with the Wilcoxon Rank Sum test, as their concentrationswere not normally distributed. For any marker result that wasunmeasurable, we utilized a standard approach of imputing concentrations50% below the limit of detection.

Table 17 below shows biomarker concentrations and their prognosticassociation that differ between those in the validation set (N=278) witha major adverse cardiac event (MACE) from 3-365 days of the blood drawand those who did not. The numbers in this table were calculated usingthe composite endpoint of one-year MACE with CV death, MI, or majorstroke; these proteins produce similar results with the compositeendpoint of one-year MACE with all-cause death, MI and/or major stroke.

TABLE 17 Prognostic Biomarkers for Prognostic Panel FM160/02, Example 4(3-365 Days Post- Cath, Received Coronary Cath; Peripheral CathOptional) (Validation Set) Concentration in Concentration in Subjectswith Subjects without One-Year MACE One-Year MACE Biomarker (N = 36) (N= 242) p-value Adiponectin (ug/mL) 4.55 (3.475, 6.325) 3.8 (2.4, 6.275)0.072 Alpha-1-Antitrypsin (AAT) 2.1 (1.9, 2.425) 1.7 (1.5, 2.075) <0.001(mg/mL) Alpha-2-Macroglobulin 2.05 (1.575, 2.525) 1.8 (1.5, 2.2) 0.036(A2Macro) (mg/mL) Angiopoietin-1 (ANG-1) 6.4 (5, 10.2) 6.9 (4.9, 10)0.797 (ng/mL) Angiotensin-Converting 79.5 (67.8, 101.5) 80.5 (62, 106.8)1 Enzyme (ACE) (ng/mL) Apolipoprotein(a) (Lp(a)) 553.5 (191.8, 881)197.5 (63.5, 536) <0.001 (ug/mL) Apolipoprotein A-I (Apo A-I) 1.65 (1.3,2.1) 1.8 (1.5, 2.2) 0.105 (mg/mL) Apolipoprotein A-II (Apo A-II) 273.5(226.8, 367.5) 318 (262.5, 392) 0.037 (ng/mL) Apolipoprotein B (Apo B)1325 (922.8, 1700) 1440 (1155, 1848) 0.159 (ug/mL) Apolipoprotein C-I(Apo C-I) 313 (248.8, 366.5) 323.5 (261.5, 382.5) 0.253 (ng/mL)Apolipoprotein C-III (Apo C-III) 194 (151.2, 263.2) 214.5 (163.8, 266)0.695 (ug/mL) Apolipoprotein H (Apo H) 356 (290.8, 428.5) 331 (274.2,397.8) 0.155 (ug/mL) Beta-2-Microglobulin (B2M) 2.55 (1.975, 3.65) 1.7(1.3, 2.2) <0.001 (ug/mL) Brain-Derived Neurotrophic 1.75 (0.98, 3.625)2.4 (1.125, 4.675) 0.237 Factor (BDNF) (ng/mL) C-Reactive Protein (CRP)8.55 (3.6, 25) 3.6 (1.4, 9.375) <0.001 (ug/mL) Carbonic anhydrase 9(CA-9) 0.21 (0.135, 0.29) 0.13 (0.076, 0.21) 0.001 (ng/mL)Carcinoembryonic antigen- 24 (22, 32.2) 24 (20, 28) 0.158 related celladhesion molecule 1 (CEACAM1) (ng/mL) CD5 Antigen-like (CD5L) 4515(3208, 5472) 3735 (2760, 4925) 0.06 (ng/mL) Decorin (ng/mL) 2.7 (2.375,3.7) 2.3 (1.9, 3.2) 0.041 E-Selectin (ng/mL) 5.6 (3.1, 7.8) 5.4 (3.9,6.7) 0.934 EN-RAGE (ng/mL) 28.5 (16.2, 59.5) 28.5 (17.2, 52.8) 0.859Eotaxin-1 (pg/mL) 91.5 (42.5, 136.5) 97 (42.5, 148) 0.397 Factor VII(ng/mL) 453.5 (337.8, 584) 474.5 (357.8, 592.8) 0.726 Ferritin (FRTN)(ng/mL) 124 (50.8, 248.5) 143 (75.2, 262) 0.434 Fetuin-A (ug/mL) 688.5(493.8, 771.2) 693.5 (574.2, 832.5) 0.356 Fibrinogen (mg/mL) 4.85 (3.9,6.825) 4.5 (3.5, 5.475) 0.009 Follicle-Stimulating Hormone 11 (5.3,38.8) 7 (4.2, 25.5) 0.074 (FSH) (mIU/mL) Growth Hormone (GH) 0.315(0.07, 1.1) 0.37 (0.15, 1.2) 0.476 (ng/mL) Haptoglobin (mg/mL) 1.15(0.648, 3.025) 1.1 (0.482, 1.8) 0.125 Immunoglobulin A (IgA) 2.7 (1.8,3.775) 2.25 (1.6, 3.175) 0.199 (mg/mL) Immunoglobulin M (IgM) 1.1(0.695, 1.725) 1.4 (0.922, 2) 0.084 (mg/mL) Insulin (uIU/mL) 1.3 (0.608,2.35) 0.635 (0.11, 1.875) 0.01 Intercellular Adhesion 116 (98.5, 174.8)101.5 (83, 126) 0.002 Molecule 1 (ICAM-1) (ng/mL) Interferon gammaInduced 369.5 (281.8, 482.5) 286 (219, 400) 0.02 Protein 10 (IP-10)(pg/mL) Interleukin-1 receptor 129.5 (94.5, 172.2) 114 (82.2, 146) 0.073antagonist (IL-1ra) (pg/mL) Interleukin-6 receptor (IL-6r) 24 (17.8, 33)24 (19, 30) 0.693 (ng/mL) Interleukin-8 (IL-8) (pg/mL) 7 (6.2, 9.9) 6.5(4, 9.2) 0.022 Interleukin-12 Subunit p40 (IL- 0.65 (0.552, 0.882) 0.57(0.45, 0.708) 0.016 12p40) (ng/mL) Interleukin-15 (IL-15) (ng/mL) 0.625(0.48, 0.745) 0.56 (0.452, 0.67) 0.119 Interleukin-18 (IL-18) (pg/mL)236 (180, 326.8) 191.5 (141.2, 255.2) 0.012 Interleukin-18-bindingprotein 14.5 (11.8, 17.2) 9 (6.8, 12) <0.001 (IL-18bp) (ng/mL)Interleukin-23 (IL-23) (ng/mL) 2.75 (2.075, 3.225) 2.6 (2, 3.175) 0.502Kidney Injury Molecule-1 0.064 (0.042, 0.18) 0.034 (0.014, 0.061) <0.001(KIM-1) (ng/mL) Leptin (ng/mL) 11 (5.9, 26.5) 7.9 (4.3, 17) 0.135Luteinizing Hormone (LH) 9.4 (4.4, 16.2) 4.8 (3.3, 9) 0.002 (mIU/mL)Macrophage Colony- 0.72 (0.428, 1.425) 0.42 (0.16, 0.62) <0.001Stimulating Factor 1 (M-CSF) (ng/mL) Macrophage Inflammatory 278.5(236.5, 380.2) 264.5 (202.5, 359.8) 0.197 Protein-1 beta (MIP-1 beta)(pg/mL) Matrix Metalloproteinase-2 1500 (1270, 1612) 1285 (1050, 1608)0.032 (MMP-2) (ng/mL) Matrix Metalloproteinase-3 8 (5.5, 12.2) 6.4 (4.5,9.3) 0.048 (MMP-3) (ng/mL) Matrix Metalloproteinase-7 0.48 (0.27, 0.735)0.32 (0.22, 0.5) 0.003 (MMP-7) (ng/mL) Matrix Metalloproteinase-9 127.5(85.5, 170.8) 125.5 (91, 187.2) 0.834 (MMP-9) (ng/mL) MatrixMetalloproteinase-9, 596.5 (413, 845.8) 594.5 (434.5, 858) 0.886 total(MMP-9, total) (ng/mL) Midkine (ng/mL) 20 (15, 27.2) 13 (9.5, 20) <0.001Monocyte Chemotactic 107.5 (75.5, 159) 107.5 (75.2, 151) 0.813 Protein 1(MCP-1) (pg/mL) Monocyte Chemotactic 25 (16.8, 29.5) 24 (18, 30.8) 0.766Protein 2 (MCP-2) (pg/mL) Monocyte Chemotactic 2075 (1640, 3288) 2240(1532, 3215) 0.906 Protein 4 (MCP-4) (pg/mL) Monokine Induced by Gamma1595 (1128, 2642) 856.5 (531, 1498) <0.001 Interferon (MIG) (pg/mL)Myeloid Progenitor Inhibitory 1.65 (1.175, 2.1) 1.2 (1, 1.6) 0.003Factor 1 (MPIF-1) (ng/mL) Myoglobin (ng/mL) 42.5 (30, 65.2) 31.5 (22,44) 0.002 N-terminal prohormone of 4665 (1822, 15980) 1275 (520.2, 3805)<0.001 brain natriuretic peptide (NT proBNP) (pg/mL) Osteopontin (ng/mL)45 (33.8, 72.2) 27 (19, 39) <0.001 Pancreatic Polypeptide (PPP) 137(83.5, 227.8) 80 (49, 149.5) <0.001 (pg/mL) Plasminogen Activator 45(24, 68.2) 45 (27.2, 71.8) 0.945 Inhibitor 1 (PAI-1) (ng/mL) Plateletendothelial cell 53.5 (45.8, 69) 54 (46, 67) 0.67 adhesion molecule(PECAM-1) (ng/mL) Prolactin (PRL) (ng/mL) 7.7 (5.7, 11.2) 7.6 (5.2, 13)0.835 Pulmonary and Activation- 145.5 (116.8, 199) 94.5 (72, 127.8)<0.001 Regulated Chemokine (PARC) (ng/mL) Pulmonary surfactant- 6.2(4.2, 10.4) 5 (3.4, 8.4) 0.072 associated protein D (SP-D) (ng/mL)Resistin (ng/mL) 3.2 (1.9, 4.9) 2.5 (1.8, 3.475) 0.069 Serotransferrin(Transferrin) 251.5 (222.5, 289) 267.5 (227.8, 314.8) 0.211 (mg/dl)Serum Amyloid P-Component 13 (10.7, 17) 13 (9.9, 17) 0.639 (SAP) (ug/mL)Stem Cell Factor (SCF) (pg/mL) 421.5 (318.8, 599.2) 361.5 (280.2, 454.5)0.016 T-Cell-Specific Protein RANTES 8.2 (5.4, 12.8) 8.9 (3.9, 16.8)0.969 (RANTES) (ng/mL) Tamm-Horsfall Urinary 0.023 (0.017, 0.031) 0.032(0.022, 0.043) <0.001 Glycoprotein (THP) (ug/mL) Thrombomodulin (TM) 4.8(3.75, 6.275) 3.8 (3.1, 4.6) <0.001 (ng/mL) Thrombospondin-1 (ng/mL)4450 (2520, 7698) 4530 (2215, 7390) 0.895 Thyroid-Stimulating Hormone1.3 (0.76, 1.825) 1.2 (0.752, 1.9) 0.677 (TSH) (uIU/mL)Thyroxine-Binding Globulin 40.5 (33.8, 46.2) 37 (31, 44) 0.124 (TBG)(ug/mL) Tissue Inhibitor of 92.5 (80, 114.5) 70 (58, 89) <0.001Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin (TTR) (mg/dl) 22(17.8, 29.5) 26 (22, 31) 0.05 Troponin (pg/ml) 21.4 (9.2, 85) 7.7 (3.5,32.5) 0.003 Tumor necrosis factor 9.5 (7.9, 15.2) 6.3 (4.6, 8.4) <0.001receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 686.5 (590.5, 815.5)541 (439, 705.5) <0.001 Molecule-1 (VCAM-1) (ng/mL) Vascular EndothelialGrowth 107.5 (77, 174.5) 96 (65.2, 132) 0.105 Factor (VEGF) (pg/mL)Vitamin D-Binding Protein 226.5 (179.5, 287.8) 250.5 (187.2, 317) 0.358(VDBP) (ug/mL) Vitamin K-Dependent Protein 13.5 (11.8, 16.2) 14 (11, 17)0.914 S (VKDPS) (ug/mL) Vitronectin (ug/mL) 500 (389.5, 635) 445.5(335.5, 563) 0.038 von Willebrand Factor (vWF) 179 (142, 262.8) 127(95.2, 171.8) <0.001 (ug/mL)

Table 18 below shows baseline clinical variables and their prognosticassociation that differ between those in the validation set (N=278) witha major adverse cardiac event (MACE) from 3-365 days of the blood drawand those who did not. The numbers in this table were calculated usingthe composite endpoint of one-year MACE with CV death, MI, or majorstroke; these proteins produce similar results with the compositeendpoint of one-year MACE with all-cause death, MI and/or major stroke.

TABLE 18 Prognostic Clinical Variables for Prognostic Panel FM160/02Example 4, (3-365 Days Post-Cath, Received Coronary Cath; PeripheralCath Optional) (Validation Set) Subjects with Subjects without One-YearMACE One-Year MACE Clinical Characteristics (N = 36) (N = 242) p-valueDemographics Age (years) 71.6 (12.2) 66.3 (11.5) 0.018 Male sex 26/36(72.2%) 173/242 (71.5%) 1 Caucasian 33/36 (91.7%) 229/242 (94.6%) 0.445Vital Signs Heart rate (beat/min) 70.1 (12) 68.6 (14.2) 0.5 Systolic BP(mmHg) 131 (26.1) 135.7 (21.6) 0.311 Diastolic BP (mmHg) 68.3 (10.3)72.7 (11.5) 0.025 Medical History Smoking 1/36 (2.8%) 40/239 (16.7%)0.024 Atrial fibrillation/flutter 8/36 (22.2%) 50/242 (20.7%) 0.827Hypertension 32/36 (88.9%) 168/242 (69.4%) 0.016 Coronary artery disease25/36 (69.4%) 110/242 (45.5%) 0.008 Myocardial infarction 12/36 (33.3%)50/242 (20.7%) 0.131 Heart failure 13/36 (36.1%) 42/242 (17.4%) 0.013Peripheral artery disease 16/36 (44.4%) 31/242 (12.8%) <0.001 COPD 5/36(13.9%) 38/242 (15.7%) 1 Diabetes, Type 1 0/36 (0%) 3/242 (1.2%) 1Diabetes, Type 2 18/36 (50%) 50/242 (20.7%) <0.001 Any Diabetes 18/36(50%) 52/242 (21.5%) <0.001 CVA/TIA 6/36 (16.7%) 23/242 (9.5%) 0.237Chronic kidney disease 8/36 (22.2%) 26/242 (10.7%) 0.059 Hemodialysis0/36 (0%) 6/242 (2.5%) 1 Angioplasty, peripheral 7/36 (19.4%) 16/242(6.6%) 0.018 and/or coronary Stent, peripheral and/or 14/36 (38.9%)51/242 (21.1%) 0.033 coronary CABG 15/36 (41.7%) 34/242 (14%) <0.001Percutaneous coronary 10/36 (27.8%) 80/242 (33.1%) 0.573 interventionMedications ACE-I/ARB 22/36 (61.1%) 123/240 (51.2%) 0.288 Beta blocker27/36 (75%) 157/241 (65.1%) 0.264 Aldosterone antagonist 5/36 (13.9%)8/241 (3.3%) 0.017 Loop diuretics 14/36 (38.9%) 43/241 (17.8%) 0.007Nitrates 15/36 (41.7%) 39/240 (16.2%) 0.001 CCB 14/36 (38.9%) 59/242(24.4%) 0.071 Statin 28/36 (77.8%) 170/241 (70.5%) 0.433 Aspirin 26/36(72.2%) 182/242 (75.2%) 0.684 Warfarin 7/36 (19.4%) 43/241 (17.8%) 0.817Clopidogrel 12/36 (33.3%) 53/241 (22%) 0.143 Echocardiographic resultsLVEF (%) 50.4 (17.7) 56.7 (14.5) 0.082 RSVP (mmHg) 42.4 (10.8) 41.8(12.4) 0.849 Stress test results Ischemia on Scan 7/9 (77.8%) 41/52(78.8%) 1 Ischemia on ECG 0/4 (0%) 25/43 (58.1%) 0.041 Angiographyresults >=70% coronary stenosis 17/36 (47.2%) 87/242 (36%) 0.201 in >=2vessels >=70% coronary stenosis 11/36 (30.6%) 46/242 (19%) 0.123 in >=3vessels Lab Measures Sodium 138.3 (2.9) 139.4 (3.4) 0.073 Blood ureanitrogen 22 (17, 30) 18 (15, 23) 0.023 (mg/dL) Creatinine (mg/dL) 1.3(1.1, 1.5) 1.1 (0.9, 1.3) <0.001 eGFR (median, CKDEPI) 70.9 (49.3, 92.7)100.7 (78, 111) <0.001 Total cholesterol (mg/dL) 136.2 (42.7) 161.1(44.1) 0.011 LDL cholesterol (mg/dL) 75.5 (33.5) 91.8 (37) 0.033Glycohemoglobin (%) 6.5 (6.1, 6.8) 6.1 (5.7, 6.6) 0.247 Glucose (mg/dL)110 (101, 147) 102 (92, 118.2) 0.022 HGB (mg/dL) 12.3 (1.7) 13.3 (1.6)0.005

Table 19 below shows the operating characteristics of the FM160/02 MACEprognostic algorithm across various scores. For sensitivity andspecificity, the 95% confidence interval is listed in parentheses. Atthe optimal score cut-point, we found 63.9% sensitivity, 77.3%specificity, PPV of 29.5% and NPV of 93.5% for composite endpoint ofone-year MACE with CV death, MI, or major stroke.

TABLE 19 Performance of Prognostic Panel FM160/02, Example 4 (3-365 DaysPost-Cath, Received Coronary Cath; Peripheral Cath Optional) (ValidationSet) Cutoff Sensitivity Specificity PPV NPV 1 0 (0, 0) 1 (1, 1) — 0.8710.5 0 (0, 0) 0.988 (0.974, 1.002) 0 0.869 0 0.111 (0.008, 0.214) 0.967(0.944, 0.989) 0.333 0.88 −0.5 0.278 (0.131, 0.424) 0.942 (0.913, 0.972)0.417 0.898 −1 0.389 (0.23, 0.548) 0.905 (0.868, 0.942) 0.378 0.909 −1.50.528 (0.365, 0.691) 0.86 (0.816, 0.903) 0.358 0.924 −2 0.667 (0.513,0.821) 0.748 (0.693, 0.803) 0.282 0.938 −2.5 0.889 (0.786, 0.992) 0.624(0.563, 0.685) 0.26 0.974 −3 0.917 (0.826, 1.007) 0.475 (0.412, 0.538)0.206 0.975 −3.5 0.944 (0.87, 1.019) 0.277 (0.22, 0.333) 0.163 0.971 −41 (1, 1) 0.128 (0.086, 0.17) 0.146 1 −4.5 1 (1, 1) 0.058 (0.028, 0.087)0.136 1 −5 1 (1, 1) 0.004 (−0.004, 0.012) 0.13 1 −5.5 1 (1, 1) 0 (0, 0)0.129 —

Table 20 below shows the scoring model using a three-level scoringsystem, and illustrates the performance of the model when the rawprognostic value is partitioned into a three-level score, each optimizedfor different operating characteristics and prognostic confidence, witha higher score indicating an increased risk for composite MACE. In athree-level score, a score of 1 indicates a low risk or negativeprognosis, a score of 3 indicates a high risk or positive prognosis, anda score of 2 indicates a prognosis of moderate risk. The cutoff forscore level 1 was optimized for NPV of 0.97 in the training set, and thecutoff for score level of 3 was optimized for a PPV of 0.45 in thetraining set.

TABLE 20 Performance of 3-Level Score for Prognostic Panel FM160/02,Example 4 (3-365 Days Post-Cath, Received Coronary Cath; Peripheral CathOptional) (Validation Set) Optimized For Observed in Validation SetScore # Patients PPV NPV PPV NPV 3 33 0.45 — 0.394 — 2 127 NA NA 0.1570.843 1 118 — 0.97 — 0.975

Example 5: A Biomarker Scoring System to Predict One Year (0 Day-365Day) Risk of Composite Cardiovascular Death, Myocardial Infarct orStroke (FM96/04)

This example demonstrates a non-invasive method employing a biomarkerscoring system that offers, among other things, high accuracy inproviding a prognosis of one year risk (0-365 day) of MACE composite ofcardiovascular death, myocardial infarct, or stroke. This exampleutilized the same methods described in Example 1 (study design,subjects, data acquisition, follow up, biomarker testing, statistics andresults (Tables 2A, 4A, 21, 22, 23, and 24 and FIG. 31).

The patients selected for analysis consisted of the chronologicallyinitial 928 patients who received a coronary angiogram. Patients mayhave also received a peripheral angiogram concomitantly.

The 928 patients selected for analysis were randomly split into atraining set (70%, or N=649, Table 2A) and a holdout validation set(30%, or N=279, Table 21).

Baseline characteristics between those with and without MACE werecompared; dichotomous variables were compared using two-sided Fishersexact test, while continuous variables were compared using two-sidedtwo-sample T test. The biomarkers compared were tested with the WilcoxonRank Sum test, as their concentrations were not normally distributed.For any marker result that was unmeasurable, we utilized a standardapproach of imputing concentrations 50% below the limit of detection.

Table 21 below shows biomarker concentrations and their prognosticassociation that differ between those in the validation set (N=278) witha major adverse cardiac event (MACE) from 0-365 days and those who didnot. The numbers in this table were calculated using the compositeendpoint of one-year MACE with CV death, MI, or major stroke; theseproteins produce similar results with the composite endpoint of one-yearMACE with all-cause death, MI and/or major stroke.

TABLE 21 Prognostic Biomarkers for Prognostic Panel FM96/04, Example 5(0-365 Days Post- Cath, Received Coronary Cath; Peripheral CathOptional) (Validation Set) Concentration in Concentration in Subjectswith Subjects without One-Year MACE One-Year MACE Biomarker (N = 39) (N= 239) p-value Adiponectin (ug/mL) 4.5 (3.4, 6.3) 3.8 (2.4, 6.3) 0.17Alpha-1-Antitrypsin (AAT) 2.1 (1.75, 2.45) 1.7 (1.5, 2.05) <0.001(mg/mL) Alpha-2-Macroglobulin 2 (1.5, 2.5) 1.8 (1.5, 2.2) 0.132(A2Macro) (mg/mL) Angiopoietin-1 (ANG-1) 7.1 (5, 11) 6.8 (4.9, 9.8)0.798 (ng/mL) Angiotensin-Converting 79 (66.5, 100) 81 (62, 108) 0.849Enzyme (ACE) (ng/mL) Apolipoprotein(a) (Lp(a)) 586 (204.5, 924) 190(62.5, 534) <0.001 (ug/mL) Apolipoprotein A-I (Apo A-I) 1.7 (1.3, 2.1)1.8 (1.5, 2.2) 0.186 (mg/mL) Apolipoprotein A-II (Apo A-II) 290 (233,364) 318 (262, 393.5) 0.045 (ng/mL) Apolipoprotein B (Apo B) 1430 (985,1790) 1430 (1140, 1835) 0.463 (ug/mL) Apolipoprotein C-I (Apo C-I) 313(256, 361.5) 325 (260.5, 383) 0.253 (ng/mL) Apolipoprotein C-III (ApoC-III) 197 (156, 249) 215 (162, 267.5) 0.717 (ug/mL) Apolipoprotein H(Apo H) 351 (289.5, 429) 331 (274, 397.5) 0.138 (ug/mL)Beta-2-Microglobulin (B2M) 2.4 (1.75, 3.5) 1.7 (1.3, 2.25) <0.001(ug/mL) Brain-Derived Neurotrophic 2 (1.05, 4) 2.4 (1.1, 4.6) 0.548Factor (BDNF) (ng/mL) C-Reactive Protein (CRP) 8.2 (3.1, 25) 3.6 (1.4,9.25) 0.001 (ug/mL) Carbonic anhydrase 9 (CA-9) 0.24 (0.145, 0.315) 0.12(0.076, 0.21) <0.001 (ng/mL) Carcinoembryonic antigen- 24 (22, 34) 24(20, 28) 0.119 related cell adhesion molecule 1 (CEACAM1) (ng/mL) CD5Antigen-like (CD5L) 4560 (3280, 5515) 3720 (2740, 4900) 0.021 (ng/mL)Decorin (ng/mL) 2.7 (2.4, 3.7) 2.3 (1.9, 3.2) 0.013 E-Selectin (ng/mL)5.7 (3.2, 7.5) 5.4 (3.9, 6.7) 0.949 EN-RAGE (ng/mL) 38 (18.5, 69.5) 28(17, 51) 0.415 Eotaxin-1 (pg/mL) 93 (42.5, 138) 97 (42.5, 148) 0.56Factor VII (ng/mL) 433 (348, 579.5) 475 (358.5, 593) 0.499 Ferritin(FRTN) (ng/mL) 131 (54.5, 255) 143 (74, 255.5) 0.67 Fetuin-A (ug/mL) 689(515.5, 825.5) 693 (573, 825.5) 0.599 Fibrinogen (mg/mL) 4.7 (4, 6.8)4.5 (3.5, 5.45) 0.007 Follicle-Stimulating Hormone 9.8 (4.3, 35.5) 7.2(4.3, 26.5) 0.281 (FSH) (mIU/mL) Growth Hormone (GH) 0.38 (0.115, 1.1)0.37 (0.15, 1.2) 0.756 (ng/mL) Haptoglobin (mg/mL) 1.1 (0.57, 2.95) 1.1(0.485, 1.85) 0.27 Immunoglobulin A (IgA) 2.7 (1.8, 3.65) 2.3 (1.6, 3.2)0.26 (mg/mL) Immunoglobulin M (IgM) 1.3 (0.78, 1.8) 1.3 (0.92, 2) 0.237(mg/mL) Insulin (uIU/mL) 1.3 (0.405, 2.3) 0.64 (0.11, 1.9) 0.03Intercellular Adhesion 115 (95.5, 172.5) 102 (83, 126) 0.005 Molecule 1(ICAM-1) (ng/mL) Interferon gamma Induced 364 (264, 465) 286 (219, 400)0.036 Protein 10 (IP-10) (pg/mL) Interleukin-1 receptor 129 (98.5, 170)113 (82, 146.5) 0.059 antagonist (IL-1ra) (pg/mL) Interleukin-6 receptor(IL-6r) 24 (18.5, 33) 24 (19, 30) 0.657 (ng/mL) Interleukin-8 (IL-8)(pg/mL) 7.1 (6.3, 10.3) 6.4 (4, 9.1) 0.012 Interleukin-12 Subunit p40(IL- 0.64 (0.545, 0.845) 0.57 (0.45, 0.705) 0.013 12p40) (ng/mL)Interleukin-15 (IL-15) (ng/mL) 0.62 (0.49, 0.73) 0.56 (0.45, 0.67) 0.088Interleukin-18 (IL-18) (pg/mL) 239 (178, 339) 191 (141.5, 251.5) 0.007Interleukin-18-binding protein 14 (9.9, 17) 9 (6.8, 12) <0.001 (IL-18bp)(ng/mL) Interleukin-23 (IL-23) (ng/mL) 2.8 (2.2, 3.25) 2.6 (2, 3.15)0.351 Kidney Injury Molecule-1 0.063 (0.038, 0.16) 0.035 (0.014, 0.062)<0.001 (KIM-1) (ng/mL) Leptin (ng/mL) 9.6 (5.7, 24.5) 8 (4.3, 17.5)0.255 Luteinizing Hormone (LH) 8.3 (3.7, 16) 4.9 (3.3, 9) 0.013 (mIU/mL)Macrophage Colony- 0.72 (0.425, 1.25) 0.42 (0.16, 0.615) <0.001Stimulating Factor 1 (M-CSF) (ng/mL) Macrophage Inflammatory 278 (234,388.5) 264 (200.5, 358.5) 0.167 Protein-1 beta (MIP-1 beta) (pg/mL)Matrix Metalloproteinase-2 1480 (1260, 1615) 1290 (1050, 1605) 0.05(MMP-2) (ng/mL) Matrix Metalloproteinase-3 7.7 (5.4, 12) 6.4 (4.5, 9.3)0.066 (MMP-3) (ng/mL) Matrix Metalloproteinase-7 0.44 (0.26, 0.73) 0.33(0.22, 0.505) 0.009 (MMP-7) (ng/mL) Matrix Metalloproteinase-9 133 (89,187) 125 (90.5, 184) 0.674 (MMP-9) (ng/mL) Matrix Metalloproteinase-9,638 (426, 874) 590 (431.5, 851.5) 0.697 total (MMP-9, total) (ng/mL)Midkine (ng/mL) 19 (13, 26) 13 (9.6, 20) <0.001 Monocyte Chemotactic 111(73, 159) 107 (75.5, 151) 0.854 Protein 1 (MCP-1) (pg/mL) MonocyteChemotactic 25 (16, 30) 24 (18.5, 30.5) 0.663 Protein 2 (MCP-2) (pg/mL)Monocyte Chemotactic 2060 (1640, 3395) 2270 (1535, 3210) 0.869 Protein 4(MCP-4) (pg/mL) Monokine Induced by Gamma 1540 (1003, 2545) 857 (534,1510) <0.001 Interferon (MIG) (pg/mL) Myeloid Progenitor Inhibitory 1.6(1.1, 2.1) 1.2 (1, 1.6) 0.007 Factor 1 (MPIF-1) (ng/mL) Myoglobin(ng/mL) 39 (30, 63) 32 (22, 44) 0.006 N-terminal prohormone of 4400(1685, 15980) 1270 (521.5, 3835) <0.001 brain natriuretic peptide (NTproBNP) (pg/mL) Osteopontin (ng/mL) 43 (29, 70) 27 (19, 39) <0.001Pancreatic Polypeptide (PPP) 119 (82, 221.5) 80 (49, 151) 0.002 (pg/mL)Plasminogen Activator 50 (26.5, 68.5) 44 (27, 71.5) 0.6 Inhibitor 1(PAI-1) (ng/mL) Platelet endothelial cell 54 (46.5, 67.5) 54 (46, 67.5)0.62 adhesion molecule (PECAM-1) (ng/mL) Prolactin (PRL) (ng/mL) 8 (5.7,11.5) 7.6 (5.2, 13) 0.692 Pulmonary and Activation- 135 (112.5, 197) 96(72, 128.5) <0.001 Regulated Chemokine (PARC) (ng/mL) Pulmonarysurfactant- 6.2 (4.1, 10.4) 5 (3.4, 8.4) 0.079 associated protein D(SP-D) (ng/mL) Resistin (ng/mL) 3.1 (1.9, 4.75) 2.5 (1.8, 3.45) 0.054Serotransferrin (Transferrin) 249 (225, 288.5) 268 (226.5, 315.5) 0.119(mg/dl) Serum Amyloid P-Component 13 (10.5, 17) 13 (9.8, 17) 0.793 (SAP)(ug/mL) Stem Cell Factor (SCF) (pg/mL) 417 (316.5, 595.5) 362 (280,456.5) 0.028 T-Cell-Specific Protein RANTES 8.3 (5.2, 13.5) 8.6 (3.8,16.5) 0.878 (RANTES) (ng/mL) Tamm-Horsfall Urinary 0.026 (0.018, 0.031)0.032 (0.022, 0.042) 0.002 Glycoprotein (THP) (ug/mL) Thrombomodulin(TM) 4.7 (3.6, 6.05) 3.8 (3.1, 4.6) <0.001 (ng/mL) Thrombospondin-1(ng/mL) 4630 (2765, 8155) 4440 (2200, 7300) 0.533 Thyroid-StimulatingHormone 1.3 (0.76, 2.05) 1.2 (0.755, 1.9) 0.526 (TSH) (uIU/mL)Thyroxine-Binding Globulin 40 (33.5, 46.5) 37 (31, 44) 0.115 (TBG)(ug/mL) Tissue Inhibitor of 92 (78, 114.5) 70 (58, 89) <0.001Metalloproteinases 1 (TIMP-1) (ng/mL) Transthyretin (TTR) (mg/dl) 23(18, 30.5) 26 (21.5, 31) 0.095 Troponin (pg/ml) 22.1 (8.8, 167.4) 7.3(3.5, 30.8) <0.001 Tumor necrosis factor 9.2 (7, 15) 6.3 (4.6, 8.7)<0.001 receptor 2 (TNFR2) (ng/mL) Vascular Cell Adhesion 676 (576, 807)541 (438.5, 707) <0.001 Molecule-1 (VCAM-1) (ng/mL) Vascular EndothelialGrowth 106 (74, 170) 95 (65.5, 132) 0.117 Factor (VEGF) (pg/mL) VitaminD-Binding Protein 236 (185, 281) 250 (185.5, 317) 0.457 (VDBP) (ug/mL)Vitamin K-Dependent Protein 14 (12, 17) 14 (11, 17) 0.827 S (VKDPS)(ug/mL) Vitronectin (ug/mL) 503 (398.5, 629) 442 (333.5, 558.5) 0.017von Willebrand Factor (vWF) 176 (137, 259.5) 127 (94.5, 171.5) <0.001(ug/mL)

Table 22 below shows baseline clinical variables and their prognosticassociation that differ between those in the validation set (N=278) witha major adverse cardiac event (MACE) from 0-365 days and those who didnot. The numbers in this table were calculated using the compositeendpoint of one-year MACE with CV death, MI, or major stroke; theseproteins produce similar results with the composite endpoint of one-yearMACE with all-cause death, MI and/or major stroke.

TABLE 22 Prognostic Clinical Variables for Prognostic Panel FM96/04,Example 5 (0-365 Days Post-Cath, Received Coronary Cath; Peripheral CathOptional) (Validation Set) Subjects with Subjects without One-Year MACEOne-Year MACE Clinical Characteristics (N = 39) (N = 239) p-valueDemographics Age (years) 70.6 (12.4) 66.4 (11.5) 0.054 Male sex 29/39(74.4%) 170/239 (71.1%) 0.848 Caucasian 36/39 (92.3%) 226/239 (94.6%)0.477 Vital Signs Heart rate (beat/min) 69.6 (11.9) 68.6 (14.2) 0.658Systolic BP (mmHg) 131.1 (25.1) 135.7 (21.7) 0.28 Diastolic BP (mmHg)68.7 (10) 72.7 (11.6) 0.028 Medical History Smoking 2/39 (5.1%) 39/236(16.5%) 0.087 Atrial fibrillation/flutter 8/39 (20.5%) 50/239 (20.9%) 1Hypertension 33/39 (84.6%) 167/239 (69.9%) 0.082 Coronary artery disease25/39 (64.1%) 110/239 (46%) 0.039 Myocardial infarction 12/39 (30.8%)50/239 (20.9%) 0.212 Heart failure 13/39 (33.3%) 42/239 (17.6%) 0.03Peripheral artery disease 16/39 (41%) 31/239 (13%) <0.001 COPD 5/39(12.8%) 38/239 (15.9%) 0.812 Diabetes, Type 1 0/39 (0%) 3/239 (1.3%) 1Diabetes, Type 2 18/39 (46.2%) 50/239 (20.9%) 0.002 Any Diabetes 18/39(46.2%) 52/239 (21.8%) 0.002 CVA/TIA 6/39 (15.4%) 23/239 (9.6%) 0.265Chronic kidney disease 8/39 (20.5%) 26/239 (10.9%) 0.111 Hemodialysis0/39 (0%) 6/239 (2.5%) 1 Angioplasty 7/39 (17.9%) 16/239 (6.7%) 0.027Angioplasty, peripheral 15/39 (38.5%) 50/239 (20.9%) 0.024 and/orcoronary Stent, peripheral and/or 15/39 (38.5%) 34/239 (14.2%) <0.001coronary Percutaneous coronary 11/39 (28.2%) 79/239 (33.1%) 0.586intervention Medications ACE-I/ARB 23/39 (59%) 122/237 (51.5%) 0.489Beta blocker 28/39 (71.8%) 156/238 (65.5%) 0.583 Aldosterone antagonist5/39 (12.8%) 8/238 (3.4%) 0.023 Loop diuretics 14/39 (35.9%) 43/238(18.1%) 0.017 Nitrates 16/39 (41%) 38/237 (16%) <0.001 CCB 14/39 (35.9%)59/239 (24.7%) 0.169 Statin 30/39 (76.9%) 168/238 (70.6%) 0.566 Aspirin28/39 (71.8%) 180/239 (75.3%) 0.691 Warfarin 7/39 (17.9%) 43/238 (18.1%)1 Clopidogrel 13/39 (33.3%) 52/238 (21.8%) 0.152 Echocardiographicresults LVEF (%) 50.9 (17.5) 56.7 (14.6) 0.095 RSVP (mmHg) 42.4 (10.8)41.8 (12.4) 0.849 Stress test results Ischemia on Scan 8/10 (80%) 40/51(78.4%) 1 Ischemia on ECG 1/5 (20%) 24/42 (57.1%) 0.171 Angiographyresults >=70% coronary stenosis 19/39 (48.7%) 85/239 (35.6%) 0.153in >=2 vessels >=70% coronary stenosis 12/39 (30.8%) 45/239 (18.8%)0.091 in >=3 vessels Lab Measures Sodium 138.2 (3.3) 139.4 (3.4) 0.055Blood urea nitrogen 21 (16.2, 30) 18 (15, 23) 0.039 (mg/dL) Creatinine(mg/dL) 1.3 (1.2, 1.5) 1.1 (0.9, 1.3) <0.001 eGFR (median, CKDEPI) 73.1(49.9, 97) 100.5 (77.8, 111) <0.001 Total cholesterol (mg/dL) 136.4(42.2) 161.4 (44.1) 0.008 LDL cholesterol (mg/dL) 75.8 (32.8) 91.9(37.2) 0.027 Glycohemoglobin (%) 6.5 (6.1, 6.8) 6.1 (5.7, 6.6) 0.247Glucose (mg/dL) 109.5 (99.8, 147.5) 102 (92, 118) 0.018 HGB (mg/dL) 12.6(1.8) 13.3 (1.6) 0.028

Table 23 below shows the operating characteristics of the FM96/04 MACEprognostic algorithm across various scores. For sensitivity andspecificity, the 95% confidence interval is listed in parentheses. Atthe optimal score cut-point, we found 66.7% sensitivity, 76.6%specificity, PPV of 31.7% and NPV of 93.4% for composite endpoint ofone-year (0-365) MACE with CV death, MI, or major stroke.

TABLE 23 Performance of Prognostic Panel FM96/04, Example 5 (0-365 DaysPost-Cath, Received Coronary Cath; Peripheral Cath Optional) (ValidationSet) Cutoff Sensitivity Specificity PPV NPV 1 0 (0, 0) 1 (1, 1) — 0.860.5 0 (0, 0) 0.992 (0.98, 1.003) 0 0.859 0 0.077 (−0.007, 0.161) 0.967(0.944, 0.989) 0.273 0.865 −0.5 0.256 (0.119, 0.393) 0.929 (0.896,0.961) 0.37 0.884 −1 0.359 (0.208, 0.51) 0.9 (0.861, 0.938) 0.368 0.896−1.5 0.487 (0.33, 0.644) 0.82 (0.771, 0.869) 0.306 0.907 −2 0.718(0.577, 0.859) 0.678 (0.619, 0.737) 0.267 0.936 −2.5 0.897 (0.802,0.993) 0.51 (0.447, 0.574) 0.23 0.968 −3 0.974 (0.925, 1.024) 0.326(0.267, 0.386) 0.191 0.987 −3.5 1 (1, 1) 0.151 (0.105, 0.196) 0.161 1 −41 (1, 1) 0.059 (0.029, 0.088) 0.148 1 −4.5 1 (1, 1) 0.004 (−0.004,0.012) 0.141 1 −5 1 (1, 1) 0 (0, 0) 0.14 —

Table 24 below shows the scoring model using a three-level scoringsystem, and illustrates the performance of the model when the rawprognostic value is partitioned into a three-level score, each optimizedfor different operating characteristics and prognostic confidence, witha higher score indicating an increased risk for composite MACE. In athree-level score, a score of 1 indicates a low risk or negativeprognosis, a score of 3 indicates a high risk or positive prognosis, anda score of 2 indicates a prognosis of moderate risk. The cutoff forscore level 1 was optimized for NPV of 0.97 in the training set, and thecutoff for score level of 3 was optimized for a PPV of 0.4 in thetraining set.

TABLE 24 Performance of 3-Level Score for Prognostic Panel FM96/04,Example 5 (0-365 Days Post-Cath, Received Coronary Cath; Peripheral CathOptional) (Validation Set) Optimized For Observed in Validation SetScore # Patients PPV NPV PPV NPV 3 38 0.4 — 0.368 — 2 168 NA NA 0.1430.857 1 71 — 0.97 — 0.986

Example 6: Further Demonstration of Methods Employing Clinical andBiomarker Analysis for the Diagnosis Cardiovascular Diseases

Table 25 is a chart of the different panels comprising proteinbiomarkers and optionally clinical variables with corresponding AUCs forthe given outcome. These reflect aforementioned Examples 1 through 5, aswell as additional panels generated using the methods and analysisprovided herein.

TABLE 25 Performance of Different Panels for Various Outcomes ComprisingProtein Biomarkers and Optionally Clinical Variables with CorrespondingAUCs and Figures Cross Validated Test Outcome/ Biomarkers & ClinicalMean Validation Figure Analysis # Positive Endpoint Variables AUCs SetAUCs Reference Diagnostic FM139/685 Diagnosis of 70% Adiponectin, 0.830.87 1, 2, 3, 4 Example 1 or > obstruction in Apolipoprotein C-I, anymajor Kidney Injury Molecule- epicardial vessels 1 (KIM-1), Midkine,History of percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement), Sex FM144/696 Diagnosis of 70%Adiponectin, 0.83 0.87 5 or > obstruction in Apolipoprotein C-1, anymajor Kidney Injury Molecule- epicardial vessels 1 (KIM-1), Midkine,History of percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement), Sex, Age FM145/701 Diagnosis of 70%Adiponectin, 0.71 0.72 6 or > obstruction in Apolipoprotein C-I, anymajor Kidney Injury Molecule- epicardial vessels 1 (KIM-1), Midkine,Sex, Age FM146/690 Diagnosis of 70% Adiponectin, 0.69 0.69 7 or >obstruction in Apolipoprotein C-I, any major Kidney Injury Molecule-epicardial vessels 1 (KIM-1), Midkine FM152/757 Diagnosis of 70%Adiponectin, Kidney 0.72 0.73 8 or > obstruction in Injury Molecule-1(KIM- any major 1), Midkine, History of epicardial vessels DiabetesMellitus Type 2, Sex, Age FM117a/657 Diagnosis of 70% Midkine, Historyof 0.80 0.84 9 or > obstruction in percutaneous coronary any majorintervention (e.g., epicardial vessels balloon angioplasty with orwithout stent placement), Sex FM139CLa/658 Diagnosis of 70% Adiponectin,History of 0.76 0.80 10 or > obstruction in percutaneous coronary anymajor intervention (e.g., epicardial vessels balloon angioplasty with orwithout stent placement), Sex FM139CLb/750 Diagnosis of 70%Apolipoprotein C-I, 0.78 0.84 11 or > obstruction in History of anymajor percutaneous coronary epicardial vessels intervention (e.g.,balloon angioplasty with or without stent placement), Sex FM139CLc/751Diagnosis of 70% Kidney Injury Molecule- 0.80 0.83 12 or > obstructionin 1 (KIM-1), History of any major percutaneous coronary epicardialvessels intervention (e.g., balloon angioplasty with or without stentplacement), Sex FM117b/663 Diagnosis of 70% Adiponectin, Midkine, 0.820.85 13 or > obstruction in History of any major percutaneous coronaryepicardial vessels intervention (e.g., balloon angioplasty with orwithout stent placement), Sex FM139CLd/752 Diagnosis of 70%Apolipoprotein C-I, 0.81 0.86 14 or > obstruction in Midkine, History ofany major percutaneous coronary epicardial vessels intervention (e.g.,balloon angioplasty with or without stent placement), Sex FM139CLe/753Diagnosis of 70% Kidney Injury Molecule- 0.81 0.85 15 or > obstructionin 1 (KIM-1), Midkine, any major History of epicardial vesselspercutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement), Sex FM139CLf/754 Diagnosis of 70% Adiponectin,0.82 0.86 16 or > obstruction in Apolipoprotein C-I, any major Midkine,History of epicardial vessels percutaneous coronary intervention (e.g.,balloon angioplasty with or without stent placement), Sex FM139CLg/755Diagnosis of 70% Adiponectin, Kidney 0.83 0.86 17 or > obstruction inInjury Molecule-1 (KIM- any major 1), Midkine, History of epicardialvessels percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement), Sex FM46/572 Diagnosis of 70%Adiponectin, Decorin, 0.84 0.84 18, 19, 20 Example 2 or > obstruction inMidkine, History of any major Myocardial Infarct (MI), epicardialvessels History of percutaneous coronary intervention (e.g., balloonangioplasty with or without stent placement), Sex FM46Fd/586 Diagnosisof 70% Adiponectin, Midkine, 0.84 0.84 21 or > obstruction in History ofMyocardial any major Infarct (MI), History of epicardial vesselspercutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement), Sex FM46Fe/587 Diagnosis of 70% Decorin,Midkine, 0.83 0.83 22 or > obstruction in History of Myocardial anymajor Infarct (MI), History of epicardial vessels percutaneous coronaryintervention (e.g., balloon angioplasty with or without stentplacement), Sex FM46Ff/588 Diagnosis of 70% Adiponectin, Decorin, 0.810.80 23 or > obstruction in History of Myocardial any major Infarct(MI), History of epicardial vessels percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), SexFM186/796 Diagnosis of 70% Adiponectin, 0.83 0.84 24 or > obstruction inInterleukin-8, Kidney any major Injury Molecule-1 (KIM- epicardialvessels 1), Stem Cell Factor, History of percutaneous coronaryintervention (e.g., balloon angioplasty with or without stentplacement), Sex, Age FM189/798 Diagnosis of 70% Adiponectin, 0.82 0.8325 or > obstruction in Interleukin-8, Kidney any major Injury Molecule-1(KIM- epicardial vessels 1), Stem Cell Factor, History of percutaneouscoronary intervention (e.g., balloon angioplasty with or without stentplacement), Sex FM187/792 Diagnosis of 70% Adiponectin, 0.83 0.85 26or > obstruction in Apolipoprotein C-1, any major Interleukin-8, Kidneyepicardial vessels Injury Molecule-1 (KIM- 1), Stem Cell Factor, Historyof percutaneous coronary intervention (e.g., balloon angioplasty with orwithout stent placement), Sex, Age FM188/794 Diagnosis of 70%Adiponectin, 0.83 0.85 27 or > obstruction in Apolipoprotein C-1, anymajor Interleukin-8, Kidney epicardial vessels Injury Molecule-1 (KIM-1), Stem Cell Factor, History of percutaneous coronary intervention(e.g., balloon angioplasty with or without stent placement), SexFM02/410 Diagnosis of 70% Adiponectin, 0.88 0.89 28 Example 3 or >obstruction in Apolipoprotein C-1, any major Matrix epicardial vesselsMetalloproteinase 9, Midkine, Myoglobin, Pulmonary Surfactant AssociatedProtein D, History of Coronary Artery Bypass Graft Surgery (CABG),History of percutaneous coronary intervention (e.g., balloon angioplastywith or without stent placement), Sex FM01/390 Diagnosis of 70%Adiponectin, Midkine, 0.90 0.87 29 or > obstruction in PulmonarySurfactant any major Associated Protein D, epicardial vessels Troponin,History of Coronary Artery Bypass Graft Surgery (CABG), History ofHemodialysis, History of Myocardial Infarct, Sex Prognostic FM160/02 1yr (3 day-365 Kidney Injury Molecule- 0.82 0.79 30 Example 4 day)Prognosis of 1 (KIM-1), N terminal composite prohormone of braincardiovascular natriuretic protein (NT- death (CVD), proBNP),Osteopontin, myocardial infarct Tissue Inhibitor of (MI), or StrokeMetalloproteinases -1 (TIMP-1) FM96/04 1 year (0 day-365 N terminalprohormone 0.77 0.77 31 Example 5 day) Prognosis of of brain natriureticcomposite protein (NT-proBNP), cardiovascular Osteopontin, Tissue death(CVD), Inhibitor of myocardial infarct Metalloproteinases -1 (MI),Stroke (TIMP-1) FM190/33 1 yr (3 day-365 N terminal prohormone 0.80 0.7832 day) Prognosis of of brain natriuretic composite protein (NT-proBNP),cardiovascular Osteopontin, Tissue death (CVD), Inhibitor of myocardialinfarct Metalloproteinases -1 (MI), or Stroke (TIMP-1) FM98/03 1 year (0day-365 N terminal prohormone 0.76 0.75 33 day) Prognosis of of brainnatriuretic composite protein (NT-proBNP), cardiovascular Osteopontindeath (CVD), myocardial infarct (MI), or Stroke FM209/02 1 yr (3 day-365Kidney Injury Molecule- 0.80 0.79 34 day) Prognosis of 1 (KIM-1), Nterminal composite All- prohormone of brain cause death natriureticprotein (NT- (ACD), myocardial proBNP), Osteopontin, infarct (MI), orTissue Inhibitor of Stroke Metalloproteinases-1 (TIMP-1) FM111/05 1 year(0 day-365 N terminal prohormone 0.78 0.77 35 day) Prognosis of of brainnatriuretic composite All- protein (NT-proBNP), cause death Osteopontin,Tissue (ACD), myocardial Inhibitor of infarct (MI), orMetalloproteinases-1 Stroke (TIMP-1) FM210/03 1 yr (3 day-365 N terminalprohormone 0.79 0.78 36 day) Prognosis of of brain natriuretic compositeAll- protein (NT-proBNP), cause death Osteopontin, Tissue (ACD),myocardial Inhibitor of infarct (MI), or Metalloproteinases-1 Stroke(TIMP-1) FM110/04 1 year (0 day-365 N terminal prohormone 0.78 0.75 37day) Prognosis of of brain natriuretic composite All- protein(NT-proBNP), cause death Osteopontin (ACD), myocardial infarct (MI), orStroke FM211/03 1 year (3 day-365 Apolipoprotein A-II, N 0.79 0.79 38day) Prognosis of terminal prohormone composite of brain natriureticcardiovascular protein (NT-proBNP), death (CVD) or Osteopontinmyocardial infarct (MI) FM77/26 1 year (0 day-365 Apolipoprotein A-II,0.78 0.77 39 day) Prognosis of Midkine, N terminal composite prohormoneof brain cardiovascular natriuretic protein (NT- death (CVD) or proBNP),Osteopontin myocardial infarct (MI) FM212/02 1 year (3 day-365Apolipoprotein A-II, 0.79 0.79 40 day) Prognosis of Midkine, N terminalcomposite prohormone of brain cardiovascular natriuretic protein (NT-death (CVD) or proBNP), Osteopontin myocardial infarct (MI) FM201/MI0021 year (3 day-365 N terminal prohormone 0.78 0.76 41 day) myocardial ofbrain natriuretic infarct (MI) protein (NT-proBNP), OsteopontinFM204/MI003 1 year (3 day-365 N terminal prohormone 0.78 0.76 42 day)myocardial of brain natriuretic infarct (MI) protein (NT-proBNP),Osteopontin, Vascular Cell Adhesion Molecule (VCAM) FM202/MI005 1 year(3 day-365 Kidney Injury Molecule- 0.80 0.75 43 day) myocardial 1(KIM-1), N terminal infarct (MI) prohormone of brain natriuretic protein(NT- proBNP), Vascular Cell Adhesion Molecule (VCAM) FM205/MI007 1 year(3 day-365 Kidney Injury Molecule- 0.78 0.75 44 day) myocardial 1(KIM-1), N terminal infarct (MI) prohormone of brain natriuretic protein(NT- proBNP), Osteopontin FM63/64 1 year (0 day-365 N terminalprohormone 0.78 0.73 45 day) myocardial of brain natriuretic infarct(MI) protein (NT-proBNP), Osteopontin FM52/244 1 year (0 day-365Apolipoprotein A-II, 0.85 0.80 46 day) Osteopontin Cardiovascular death(CVD) FM194/CVD001 1 year (3 day-365 Apolipoprotein A-II, 0.85 0.80 47day) Osteopontin Cardiovascular death (CVD) FM193/R08 1 year (3 day-365Apolipoprotein A-II, 0.86 0.80 48 day) Osteopontin, History ofCardiovascular Diabetes Mellitus Type death (CVD) 2 FM53/237 1 year (0day-365 Apolipoprotein A-II, 0.85 0.81 49 day) Midkine, OsteopontinCardiovascular death (CVD) FM195/CVD002 1 year (3 day-365 ApolipoproteinA-II, 0.84 0.81 50 day) Midkine, Osteopontin Cardiovascular death (CVD)FM207/R04 1 year (3 day-365 Apolipoprotein A-II, N 0.84 0.83 51 day)terminal prohormone Cardiovascular of brain natriuretic death (CVD)protein (NT-proBNP), Osteopontin, Tissue Inhibitor of Metalloproteinases-1 (TIMP-1) FM208/R05 1 year (3 day-365 N terminal prohormone 0.82 0.8252 day) of brain natriuretic Cardiovascular protein (NT-proBNP), death(CVD) Osteopontin, Tissue Inhibitor of Metalloproteinases -1 (TIMP-1)

1-41. (canceled)
 42. A method for treating a patient at risk ofcardiovascular disease or having chest pain or discomfort in theshoulder, arm, back, neck or jaw, comprising: (i) determining whetherthe patient suffers from obstructive coronary artery disease by:obtaining or having obtained a biological sample from the patient;performing or having performed a biomarker assay on the biologicalsample for kidney injury molecule-1, adiponectin, and either or both oftroponin and midkine; and calculating a score based on the biomarkerlevels; and (ii) identifying the patient as having a score indicative ofobstructive coronary artery disease, then administering at least onecardiac invention selected from a cardiovascular disease pharmacologicagent, cardiac catheterization, percutaneous coronary intervention, andcoronary artery bypass graft.
 43. The method of claim 42, comprisingperforming or having performed a biomarker assay on the biologicalsample for kidney injury molecule-1, adiponectin, and troponin.
 44. Themethod of claim 42, comprising performing or having performed abiomarker assay on the biological sample for kidney injury molecule-1,adiponectin, and midkine.
 45. The method of claim 42, comprisingperforming or having performed a biomarker assay on the biologicalsample for kidney injury molecule-1, adiponectin, troponin, and midkine.46. The method of claim 42, wherein the score identifies the patient ashaving at least 70% obstruction in a major epicardial vessel.
 47. Themethod of claim 42, wherein the score is based on the biomarker levelsand one or more clinical variables.
 48. The method of claim 42, whereinthe one or more clinical variables comprises two or more of thepatient's sex, the patient's age, and the patient's history ofpercutaneous coronary intervention.
 49. The method of claim 48, whereinthe score identifies the patient as having at least 70% obstruction in amajor epicardial vessel.
 50. The method of claim 42, wherein the atleast one cardiac invention comprises administration of thecardiovascular disease pharmacologic agent.
 51. The method of claim 50,wherein the cardiovascular disease pharmacologic agent is a nitrate. 52.The method of claim 50, wherein the cardiovascular disease pharmacologicagent is a beta blocker.
 53. The method of claim 50, wherein the one ormore cardiovascular disease pharmacologic agents comprise an ACEinhibitor.
 54. The method of claim 50, wherein the one or morecardiovascular disease pharmacologic agents comprise an antiplateletagent.
 55. The method of claim 50, wherein the one or morecardiovascular disease pharmacologic agents comprise a lipid-loweringagent.
 56. The method of claim 42, wherein the at least one cardiacinvention comprises cardiac catheterization.
 57. The method of claim 42,wherein the at least one cardiac invention comprises percutaneouscoronary intervention.
 58. The method of claim 42, wherein the at leastone cardiac invention comprises coronary artery bypass graft.
 59. Themethod of claim 42, wherein the score is a diagnostic score.
 60. Themethod of claim 49, wherein the score is a diagnostic score.
 61. Themethod of claim 42, wherein the one or more clinical variables comprisesall three of the patient's sex, the patient's age, and the patient'shistory of percutaneous coronary intervention.